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OpenML is a place where you can share interesting datasets with the people who love to analyse data, and build the best solutions together, saving you valuable time, increasing your visibility, and speeding up discovery. OpenML links data to algorithms and people, so you can build on the state of the art and learn to teach machines tolearn better.
OPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.OPENML
Second quartile (Median) of standard deviation of attributes of the numeric type. Number of attributes divided by the number of instances. Maximum mutual information between the nominal attributes and the target attribute. The minimal number of distinct values among attributes of the nominal type.OPENML
The buildings differ with respect to the glazing area, the glazing area distribution, and the orientation, amongst other parameters. We simulate various settings as functions of the afore-mentioned characteristics to obtain 768 building shapes. The dataset comprises 768 samples and 8 features, aiming to predict two real valuedresponses.
OPENML
A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University, 2003. #Dataset from the LIBSVM data repository Preprocessing: Original data: an astroparticle application from Jan Conrad of Uppsala University,Sweden.
OPENML
There are 4 different data sets obtained from this database: House (8H) House (8L) House (16H) House (16L) These are all concerned with predicting the median price of the house in the region based on demographic composition and a state of housing market in the region. A number in the name signifies the number of attributes of the data set.OPENML
The dataset represents 88588 sensor data samples collected from the accelerometer and gyroscope from iPhone 5c in 10 seconds intervals and ~5.4/second frequency. ### Attribute information This data is represented by following columns (each column contains sensor data for one of the sensor's axes): acceleration_x acceleration_yacceleration_z
OPENML
The Leaves were collected in the Royal Botanic Gardens, Kew, UK. email: james.cope@kingston.ac.uk (b) This dataset consists of work carried out by James Cope, Charles Mallah, and James Orwell. Donor of database Charles Mallah: charles.mallah@kingston.ac.uk; James Cope: james.cope@kingston.ac.uk ``` ### Dataset Information The originaldata
OPENML
The first 21 features (columns 2-22) are kinematic properties measured by the particle detectors in the accelerator. The last seven features are functions of the first 21 features; these are high-level features derived by physicists to help discriminate between the two classes. There is an interest in using deep learning methods to obviate theOPENML
The original thyroid disease (ann-thyroid) dataset from UCI machine learning repository is a classification dataset, which is suited for training ANNs. It has 3772 training instances and 3428 testing instances. It has 15 categorical and 6 real attributes. The problem is to determine whether a patient referred to the clinic is hypothyroid. OPENMLHELPCONTACTPLEASE CITE USNO ACCOUNT? JOIN OPENMLTASK TYPEFORGOTPASSWORD
OpenML is a place where you can share interesting datasets with the people who love to analyse data, and build the best solutions together, saving you valuable time, increasing your visibility, and speeding up discovery. OpenML links data to algorithms and people, so you can build on the state of the art and learn to teach machines tolearn better.
OPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.OPENML
Second quartile (Median) of standard deviation of attributes of the numeric type. Number of attributes divided by the number of instances. Maximum mutual information between the nominal attributes and the target attribute. The minimal number of distinct values among attributes of the nominal type.OPENML
The buildings differ with respect to the glazing area, the glazing area distribution, and the orientation, amongst other parameters. We simulate various settings as functions of the afore-mentioned characteristics to obtain 768 building shapes. The dataset comprises 768 samples and 8 features, aiming to predict two real valuedresponses.
OPENML
A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University, 2003. #Dataset from the LIBSVM data repository Preprocessing: Original data: an astroparticle application from Jan Conrad of Uppsala University,Sweden.
OPENML
There are 4 different data sets obtained from this database: House (8H) House (8L) House (16H) House (16L) These are all concerned with predicting the median price of the house in the region based on demographic composition and a state of housing market in the region. A number in the name signifies the number of attributes of the data set.OPENML
The dataset represents 88588 sensor data samples collected from the accelerometer and gyroscope from iPhone 5c in 10 seconds intervals and ~5.4/second frequency. ### Attribute information This data is represented by following columns (each column contains sensor data for one of the sensor's axes): acceleration_x acceleration_yacceleration_z
OPENML
The Leaves were collected in the Royal Botanic Gardens, Kew, UK. email: james.cope@kingston.ac.uk (b) This dataset consists of work carried out by James Cope, Charles Mallah, and James Orwell. Donor of database Charles Mallah: charles.mallah@kingston.ac.uk; James Cope: james.cope@kingston.ac.uk ``` ### Dataset Information The originaldata
OPENML
The first 21 features (columns 2-22) are kinematic properties measured by the particle detectors in the accelerator. The last seven features are functions of the first 21 features; these are high-level features derived by physicists to help discriminate between the two classes. There is an interest in using deep learning methods to obviate theOPENML
The original thyroid disease (ann-thyroid) dataset from UCI machine learning repository is a classification dataset, which is suited for training ANNs. It has 3772 training instances and 3428 testing instances. It has 15 categorical and 6 real attributes. The problem is to determine whether a patient referred to the clinic is hypothyroid.OPENML
OpenML Benchmarking Suites and the OpenML-CC18. We advocate the use of curated, comprehensive benchmark suites of machine learning datasets, backed by standardized OpenML-based interfaces and complementary software toolkits written in Python, Java and R. We demonstrate how to easily execute comprehensive benchmarking studies using standardizedOPENML
The Centre is associated with the Department of Thoracic Surgery of the Medical University of Wroclaw and Lower-Silesian Centre for Pulmonary Diseases, Poland, while the research database constitutes a part of the National Lung Cancer Registry, administered by the Institute of Tuberculosis and Pulmonary Diseases in Warsaw, Poland.OPENML
The first 21 features (columns 2-22) are kinematic properties measured by the particle detectors in the accelerator. The last seven features are functions of the first 21 features; these are high-level features derived by physicists to help discriminate between the two classes. There is an interest in using deep learning methods to obviate theOPENML
These are prepared monthly for us by Population Division here at the Census Bureau. We use 3 sets of controls. These are: 1. A single cell estimate of the population 16+ for each state. 2. Controls for Hispanic Origin by age and sex. 3. Controls by Race, age and sex. We use all three sets of controls in our weighting program and "rake"through
OPENML
The settings of high frequency filter 50 Hz, low frequency filter 1.6 Hz, notch filter 50 Hz, sensitivity 70 micro volts/mm, and a sampling rate of 256 Hz were used for the basic signal processing. Eight EEG electrodes (C3, C4, P3, P4, F3, F4, T3, and T4) were placed according to the international standard 10-20 system of electrode placementOPENML
Contains the forest cover type for 30 x 30 meter cells obtained from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. It contains 581,012 instances and 54 attributes, and it has been used in several papers on data stream classification.OPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.OPENML
Attribute Information: Each attribute (feature) corresponds to the application of a speech signal processing algorithm which aims to characterise objectively the signal. These algorithms include standard perturbation analysis methods, wavelet-based features, fundamental frequency-based features, and tools used to mine nonlineartime-series.
OPENML
Abstract: This dataset consists in a collection of shape and texture features extracted from digital images of leaf specimens originating from a total of 40 different plant species. Source: This dataset was created by Pedro F. B. Silva and Andre R. S. Marcal using leaf specimens collected by Rubim Almeida da Silva at the Faculty ofScience
OPENML
I had a list of what the 30 or so variables were, but a.) I lost it, and b.), I would not know which 13 variables are included in the set. - The attributes are: 1) Alcohol 2) Malic acid 3) Ash 4) Alcalinity of ash 5) Magnesium 6) Total phenols 7) Flavanoids 8) Nonflavanoid phenols 9) Proanthocyanins 10) Color intensity 11) Hue 12) OD280/OD315 OPENMLHELPCONTACTPLEASE CITE USNO ACCOUNT? JOIN OPENMLTASK TYPEFORGOTPASSWORD
OpenML is a place where you can share interesting datasets with the people who love to analyse data, and build the best solutions together, saving you valuable time, increasing your visibility, and speeding up discovery. OpenML links data to algorithms and people, so you can build on the state of the art and learn to teach machines tolearn better.
OPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.OPENML
This is the large soybean database from the UCI repository, with its training and test database combined into a single file. There are 19 classes, only the first 15 of which have been used in prior. 40719 runs1 likes53 downloads54 reach13 impact. 683 instances - 36 features - 19 classes - 2337 missing values.OPENML
A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University, 2003. #Dataset from the LIBSVM data repository Preprocessing: Original data: an astroparticle application from Jan Conrad of Uppsala University,Sweden.
OPENML
Second quartile (Median) of standard deviation of attributes of the numeric type. Number of attributes divided by the number of instances. Maximum mutual information between the nominal attributes and the target attribute. The minimal number of distinct values among attributes of the nominal type.OPENML
The Centre is associated with the Department of Thoracic Surgery of the Medical University of Wroclaw and Lower-Silesian Centre for Pulmonary Diseases, Poland, while the research database constitutes a part of the National Lung Cancer Registry, administered by the Institute of Tuberculosis and Pulmonary Diseases in Warsaw, Poland.OPENML
The buildings differ with respect to the glazing area, the glazing area distribution, and the orientation, amongst other parameters. We simulate various settings as functions of the afore-mentioned characteristics to obtain 768 building shapes. The dataset comprises 768 samples and 8 features, aiming to predict two real valuedresponses.
OPENML
Second quartile (Median) of mutual information between the nominal attributes and the target attribute. Percentage of instances belonging to the most frequent class. Mean standard deviation of attributes of the numeric type. Second quartile (Median) of skewness among attributes of the numeric type.OPENML
The dataset represents 88588 sensor data samples collected from the accelerometer and gyroscope from iPhone 5c in 10 seconds intervals and ~5.4/second frequency. ### Attribute information This data is represented by following columns (each column contains sensor data for one of the sensor's axes): acceleration_x acceleration_yacceleration_z
OPENML
The first 21 features (columns 2-22) are kinematic properties measured by the particle detectors in the accelerator. The last seven features are functions of the first 21 features; these are high-level features derived by physicists to help discriminate between the two classes. There is an interest in using deep learning methods to obviate the OPENMLHELPCONTACTPLEASE CITE USNO ACCOUNT? JOIN OPENMLTASK TYPEFORGOTPASSWORD
OpenML is a place where you can share interesting datasets with the people who love to analyse data, and build the best solutions together, saving you valuable time, increasing your visibility, and speeding up discovery. OpenML links data to algorithms and people, so you can build on the state of the art and learn to teach machines tolearn better.
OPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.OPENML
This is the large soybean database from the UCI repository, with its training and test database combined into a single file. There are 19 classes, only the first 15 of which have been used in prior. 40719 runs1 likes53 downloads54 reach13 impact. 683 instances - 36 features - 19 classes - 2337 missing values.OPENML
A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University, 2003. #Dataset from the LIBSVM data repository Preprocessing: Original data: an astroparticle application from Jan Conrad of Uppsala University,Sweden.
OPENML
Second quartile (Median) of standard deviation of attributes of the numeric type. Number of attributes divided by the number of instances. Maximum mutual information between the nominal attributes and the target attribute. The minimal number of distinct values among attributes of the nominal type.OPENML
The Centre is associated with the Department of Thoracic Surgery of the Medical University of Wroclaw and Lower-Silesian Centre for Pulmonary Diseases, Poland, while the research database constitutes a part of the National Lung Cancer Registry, administered by the Institute of Tuberculosis and Pulmonary Diseases in Warsaw, Poland.OPENML
The buildings differ with respect to the glazing area, the glazing area distribution, and the orientation, amongst other parameters. We simulate various settings as functions of the afore-mentioned characteristics to obtain 768 building shapes. The dataset comprises 768 samples and 8 features, aiming to predict two real valuedresponses.
OPENML
Second quartile (Median) of mutual information between the nominal attributes and the target attribute. Percentage of instances belonging to the most frequent class. Mean standard deviation of attributes of the numeric type. Second quartile (Median) of skewness among attributes of the numeric type.OPENML
The dataset represents 88588 sensor data samples collected from the accelerometer and gyroscope from iPhone 5c in 10 seconds intervals and ~5.4/second frequency. ### Attribute information This data is represented by following columns (each column contains sensor data for one of the sensor's axes): acceleration_x acceleration_yacceleration_z
OPENML
The first 21 features (columns 2-22) are kinematic properties measured by the particle detectors in the accelerator. The last seven features are functions of the first 21 features; these are high-level features derived by physicists to help discriminate between the two classes. There is an interest in using deep learning methods to obviate theOPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.OPENML
Second quartile (Median) of standard deviation of attributes of the numeric type. Number of attributes divided by the number of instances. Maximum mutual information between the nominal attributes and the target attribute. The minimal number of distinct values among attributes of the nominal type.OPENML
Electricity transfers to/from the neighboring state of Victoria were done to alleviate fluctuations. The dataset (originally named ELEC2) contains 45,312 instances dated from 7 May 1996 to 5 December 1998. Each example of the dataset refers to a period of 30 minutes, i.e. there are 48 instances for each time period of one day.OPENML
The Car Evaluation Database contains examples with the structural information removed, i.e., directly relates CAR to the six input attributes: buying, maint, doors, persons, lug_boot, safety. Because of known underlying concept structure, this database may be particularly useful for testing constructive induction and structurediscovery methods
OPENML
The first 21 features (columns 2-22) are kinematic properties measured by the particle detectors in the accelerator. The last seven features are functions of the first 21 features; these are high-level features derived by physicists to help discriminate between the two classes. There is an interest in using deep learning methods to obviate theOPENML
These are prepared monthly for us by Population Division here at the Census Bureau. We use 3 sets of controls. These are: 1. A single cell estimate of the population 16+ for each state. 2. Controls for Hispanic Origin by age and sex. 3. Controls by Race, age and sex. We use all three sets of controls in our weighting program and "rake"through
OPENML
The Leaves were collected in the Royal Botanic Gardens, Kew, UK. email: james.cope@kingston.ac.uk (b) This dataset consists of work carried out by James Cope, Charles Mallah, and James Orwell. Donor of database Charles Mallah: charles.mallah@kingston.ac.uk; James Cope: james.cope@kingston.ac.uk ``` ### Dataset Information The originaldata
OPENML
Data were extracted from images that were taken from genuine and forged banknote-like specimens. For digitization, an industrial camera usually used for print inspection was used. The final images have 400x 400 pixels. Due to the object lens and distance to the investigated object gray-scale pictures with a resolution of about 660 dpi weregained.
OPENML
I had a list of what the 30 or so variables were, but a.) I lost it, and b.), I would not know which 13 variables are included in the set. - The attributes are: 1) Alcohol 2) Malic acid 3) Ash 4) Alcalinity of ash 5) Magnesium 6) Total phenols 7) Flavanoids 8) Nonflavanoid phenols 9) Proanthocyanins 10) Color intensity 11) Hue 12) OD280/OD315OPENML
Help us complete this description Edit. Author: Source: KEEL Please cite: An artificial data set where instances belongs to several clusters with a banana shape. There are two attributes At1 and At2 corresponding to the x and y axis, respectively. The class label (-1 and 1) represents one of the two banana shapes in the dataset. OPENMLHELPCONTACTPLEASE CITE USNO ACCOUNT? JOIN OPENMLTASK TYPEFORGOTPASSWORD
OpenML is a place where you can share interesting datasets with the people who love to analyse data, and build the best solutions together, saving you valuable time, increasing your visibility, and speeding up discovery. OpenML links data to algorithms and people, so you can build on the state of the art and learn to teach machines tolearn better.
OPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.OPENML
Second quartile (Median) of standard deviation of attributes of the numeric type. Number of attributes divided by the number of instances. Maximum mutual information between the nominal attributes and the target attribute. The minimal number of distinct values among attributes of the nominal type.OPENML
A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University, 2003. #Dataset from the LIBSVM data repository Preprocessing: Original data: an astroparticle application from Jan Conrad of Uppsala University,Sweden.
OPENML
The buildings differ with respect to the glazing area, the glazing area distribution, and the orientation, amongst other parameters. We simulate various settings as functions of the afore-mentioned characteristics to obtain 768 building shapes. The dataset comprises 768 samples and 8 features, aiming to predict two real valuedresponses.
OPENML
The Centre is associated with the Department of Thoracic Surgery of the Medical University of Wroclaw and Lower-Silesian Centre for Pulmonary Diseases, Poland, while the research database constitutes a part of the National Lung Cancer Registry, administered by the Institute of Tuberculosis and Pulmonary Diseases in Warsaw, Poland.OPENML
The first 21 features (columns 2-22) are kinematic properties measured by the particle detectors in the accelerator. The last seven features are functions of the first 21 features; these are high-level features derived by physicists to help discriminate between the two classes. There is an interest in using deep learning methods to obviate theOPENML
The Leaves were collected in the Royal Botanic Gardens, Kew, UK. email: james.cope@kingston.ac.uk (b) This dataset consists of work carried out by James Cope, Charles Mallah, and James Orwell. Donor of database Charles Mallah: charles.mallah@kingston.ac.uk; James Cope: james.cope@kingston.ac.uk ``` ### Dataset Information The originaldata
OPENML
The original thyroid disease (ann-thyroid) dataset from UCI machine learning repository is a classification dataset, which is suited for training ANNs. It has 3772 training instances and 3428 testing instances. It has 15 categorical and 6 real attributes. The problem is to determine whether a patient referred to the clinic is hypothyroid.OPENML
The dataset represents 88588 sensor data samples collected from the accelerometer and gyroscope from iPhone 5c in 10 seconds intervals and ~5.4/second frequency. ### Attribute information This data is represented by following columns (each column contains sensor data for one of the sensor's axes): acceleration_x acceleration_yacceleration_z
OPENMLHELPCONTACTPLEASE CITE USNO ACCOUNT? JOIN OPENMLTASK TYPEFORGOTPASSWORD
OpenML is a place where you can share interesting datasets with the people who love to analyse data, and build the best solutions together, saving you valuable time, increasing your visibility, and speeding up discovery. OpenML links data to algorithms and people, so you can build on the state of the art and learn to teach machines tolearn better.
OPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.OPENML
Second quartile (Median) of standard deviation of attributes of the numeric type. Number of attributes divided by the number of instances. Maximum mutual information between the nominal attributes and the target attribute. The minimal number of distinct values among attributes of the nominal type.OPENML
A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University, 2003. #Dataset from the LIBSVM data repository Preprocessing: Original data: an astroparticle application from Jan Conrad of Uppsala University,Sweden.
OPENML
The buildings differ with respect to the glazing area, the glazing area distribution, and the orientation, amongst other parameters. We simulate various settings as functions of the afore-mentioned characteristics to obtain 768 building shapes. The dataset comprises 768 samples and 8 features, aiming to predict two real valuedresponses.
OPENML
The Centre is associated with the Department of Thoracic Surgery of the Medical University of Wroclaw and Lower-Silesian Centre for Pulmonary Diseases, Poland, while the research database constitutes a part of the National Lung Cancer Registry, administered by the Institute of Tuberculosis and Pulmonary Diseases in Warsaw, Poland.OPENML
The first 21 features (columns 2-22) are kinematic properties measured by the particle detectors in the accelerator. The last seven features are functions of the first 21 features; these are high-level features derived by physicists to help discriminate between the two classes. There is an interest in using deep learning methods to obviate theOPENML
The Leaves were collected in the Royal Botanic Gardens, Kew, UK. email: james.cope@kingston.ac.uk (b) This dataset consists of work carried out by James Cope, Charles Mallah, and James Orwell. Donor of database Charles Mallah: charles.mallah@kingston.ac.uk; James Cope: james.cope@kingston.ac.uk ``` ### Dataset Information The originaldata
OPENML
The original thyroid disease (ann-thyroid) dataset from UCI machine learning repository is a classification dataset, which is suited for training ANNs. It has 3772 training instances and 3428 testing instances. It has 15 categorical and 6 real attributes. The problem is to determine whether a patient referred to the clinic is hypothyroid.OPENML
The dataset represents 88588 sensor data samples collected from the accelerometer and gyroscope from iPhone 5c in 10 seconds intervals and ~5.4/second frequency. ### Attribute information This data is represented by following columns (each column contains sensor data for one of the sensor's axes): acceleration_x acceleration_yacceleration_z
OPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.OPENML
OpenML Benchmarking Suites and the OpenML-CC18. We advocate the use of curated, comprehensive benchmark suites of machine learning datasets, backed by standardized OpenML-based interfaces and complementary software toolkits written in Python, Java. 0 datasets, 0 tasks, 0flows, 0 runs.
OPENML
OpenML Benchmarking Suites and the OpenML-CC18. We advocate the use of curated, comprehensive benchmark suites of machine learning datasets, backed by standardized OpenML-based interfaces and complementary software toolkits written in Python, Java and R. We demonstrate how to easily execute comprehensive benchmarking studies using standardizedOPENML
These are prepared monthly for us by Population Division here at the Census Bureau. We use 3 sets of controls. These are: 1. A single cell estimate of the population 16+ for each state. 2. Controls for Hispanic Origin by age and sex. 3. Controls by Race, age and sex. We use all three sets of controls in our weighting program and "rake"through
OPENML
Author: Athanasios Tsanas Source: UCI Please cite: A. Tsanas, M.A. Little, C. Fox, L.O. Ramig: Objective automatic assessment of rehabilitative speech treatment in Parkinsons disease , IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 22, pp. 181-190, January 2014 Dataset title laLSVT Voice Rehabilitation Data Set Source: The dataset was created by AthanasiosTsanas
OPENML
Second quartile (Median) of mutual information between the nominal attributes and the target attribute. Percentage of instances belonging to the most frequent class. Mean standard deviation of attributes of the numeric type. Second quartile (Median) of skewness among attributes of the numeric type.OPENML
The settings of high frequency filter 50 Hz, low frequency filter 1.6 Hz, notch filter 50 Hz, sensitivity 70 micro volts/mm, and a sampling rate of 256 Hz were used for the basic signal processing. Eight EEG electrodes (C3, C4, P3, P4, F3, F4, T3, and T4) were placed according to the international standard 10-20 system of electrode placementOPENML
Contains the forest cover type for 30 x 30 meter cells obtained from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. It contains 581,012 instances and 54 attributes, and it has been used in several papers on data stream classification.OPENML
I had a list of what the 30 or so variables were, but a.) I lost it, and b.), I would not know which 13 variables are included in the set. - The attributes are: 1) Alcohol 2) Malic acid 3) Ash 4) Alcalinity of ash 5) Magnesium 6) Total phenols 7) Flavanoids 8) Nonflavanoid phenols 9) Proanthocyanins 10) Color intensity 11) Hue 12) OD280/OD315OPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning. OPENMLHELPCONTACTPLEASE CITE USNO ACCOUNT? JOIN OPENMLTASK TYPEFORGOTPASSWORD
OpenML is a place where you can share interesting datasets with the people who love to analyse data, and build the best solutions together, saving you valuable time, increasing your visibility, and speeding up discovery. OpenML links data to algorithms and people, so you can build on the state of the art and learn to teach machines tolearn better.
OPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.OPENML
Second quartile (Median) of standard deviation of attributes of the numeric type. Number of attributes divided by the number of instances. Maximum mutual information between the nominal attributes and the target attribute. The minimal number of distinct values among attributes of the nominal type.OPENML
A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University, 2003. #Dataset from the LIBSVM data repository Preprocessing: Original data: an astroparticle application from Jan Conrad of Uppsala University,Sweden.
OPENML
The buildings differ with respect to the glazing area, the glazing area distribution, and the orientation, amongst other parameters. We simulate various settings as functions of the afore-mentioned characteristics to obtain 768 building shapes. The dataset comprises 768 samples and 8 features, aiming to predict two real valuedresponses.
OPENML
The Centre is associated with the Department of Thoracic Surgery of the Medical University of Wroclaw and Lower-Silesian Centre for Pulmonary Diseases, Poland, while the research database constitutes a part of the National Lung Cancer Registry, administered by the Institute of Tuberculosis and Pulmonary Diseases in Warsaw, Poland.OPENML
The first 21 features (columns 2-22) are kinematic properties measured by the particle detectors in the accelerator. The last seven features are functions of the first 21 features; these are high-level features derived by physicists to help discriminate between the two classes. There is an interest in using deep learning methods to obviate theOPENML
The Leaves were collected in the Royal Botanic Gardens, Kew, UK. email: james.cope@kingston.ac.uk (b) This dataset consists of work carried out by James Cope, Charles Mallah, and James Orwell. Donor of database Charles Mallah: charles.mallah@kingston.ac.uk; James Cope: james.cope@kingston.ac.uk ``` ### Dataset Information The originaldata
OPENML
The original thyroid disease (ann-thyroid) dataset from UCI machine learning repository is a classification dataset, which is suited for training ANNs. It has 3772 training instances and 3428 testing instances. It has 15 categorical and 6 real attributes. The problem is to determine whether a patient referred to the clinic is hypothyroid.OPENML
The dataset represents 88588 sensor data samples collected from the accelerometer and gyroscope from iPhone 5c in 10 seconds intervals and ~5.4/second frequency. ### Attribute information This data is represented by following columns (each column contains sensor data for one of the sensor's axes): acceleration_x acceleration_yacceleration_z
OPENMLHELPCONTACTPLEASE CITE USNO ACCOUNT? JOIN OPENMLTASK TYPEFORGOTPASSWORD
OpenML is a place where you can share interesting datasets with the people who love to analyse data, and build the best solutions together, saving you valuable time, increasing your visibility, and speeding up discovery. OpenML links data to algorithms and people, so you can build on the state of the art and learn to teach machines tolearn better.
OPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.OPENML
Second quartile (Median) of standard deviation of attributes of the numeric type. Number of attributes divided by the number of instances. Maximum mutual information between the nominal attributes and the target attribute. The minimal number of distinct values among attributes of the nominal type.OPENML
A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University, 2003. #Dataset from the LIBSVM data repository Preprocessing: Original data: an astroparticle application from Jan Conrad of Uppsala University,Sweden.
OPENML
The buildings differ with respect to the glazing area, the glazing area distribution, and the orientation, amongst other parameters. We simulate various settings as functions of the afore-mentioned characteristics to obtain 768 building shapes. The dataset comprises 768 samples and 8 features, aiming to predict two real valuedresponses.
OPENML
The Centre is associated with the Department of Thoracic Surgery of the Medical University of Wroclaw and Lower-Silesian Centre for Pulmonary Diseases, Poland, while the research database constitutes a part of the National Lung Cancer Registry, administered by the Institute of Tuberculosis and Pulmonary Diseases in Warsaw, Poland.OPENML
The first 21 features (columns 2-22) are kinematic properties measured by the particle detectors in the accelerator. The last seven features are functions of the first 21 features; these are high-level features derived by physicists to help discriminate between the two classes. There is an interest in using deep learning methods to obviate theOPENML
The Leaves were collected in the Royal Botanic Gardens, Kew, UK. email: james.cope@kingston.ac.uk (b) This dataset consists of work carried out by James Cope, Charles Mallah, and James Orwell. Donor of database Charles Mallah: charles.mallah@kingston.ac.uk; James Cope: james.cope@kingston.ac.uk ``` ### Dataset Information The originaldata
OPENML
The original thyroid disease (ann-thyroid) dataset from UCI machine learning repository is a classification dataset, which is suited for training ANNs. It has 3772 training instances and 3428 testing instances. It has 15 categorical and 6 real attributes. The problem is to determine whether a patient referred to the clinic is hypothyroid.OPENML
The dataset represents 88588 sensor data samples collected from the accelerometer and gyroscope from iPhone 5c in 10 seconds intervals and ~5.4/second frequency. ### Attribute information This data is represented by following columns (each column contains sensor data for one of the sensor's axes): acceleration_x acceleration_yacceleration_z
OPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.OPENML
OpenML Benchmarking Suites and the OpenML-CC18. We advocate the use of curated, comprehensive benchmark suites of machine learning datasets, backed by standardized OpenML-based interfaces and complementary software toolkits written in Python, Java. 0 datasets, 0 tasks, 0flows, 0 runs.
OPENML
OpenML Benchmarking Suites and the OpenML-CC18. We advocate the use of curated, comprehensive benchmark suites of machine learning datasets, backed by standardized OpenML-based interfaces and complementary software toolkits written in Python, Java and R. We demonstrate how to easily execute comprehensive benchmarking studies using standardizedOPENML
These are prepared monthly for us by Population Division here at the Census Bureau. We use 3 sets of controls. These are: 1. A single cell estimate of the population 16+ for each state. 2. Controls for Hispanic Origin by age and sex. 3. Controls by Race, age and sex. We use all three sets of controls in our weighting program and "rake"through
OPENML
Author: Athanasios Tsanas Source: UCI Please cite: A. Tsanas, M.A. Little, C. Fox, L.O. Ramig: Objective automatic assessment of rehabilitative speech treatment in Parkinsons disease , IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 22, pp. 181-190, January 2014 Dataset title laLSVT Voice Rehabilitation Data Set Source: The dataset was created by AthanasiosTsanas
OPENML
Second quartile (Median) of mutual information between the nominal attributes and the target attribute. Percentage of instances belonging to the most frequent class. Mean standard deviation of attributes of the numeric type. Second quartile (Median) of skewness among attributes of the numeric type.OPENML
The settings of high frequency filter 50 Hz, low frequency filter 1.6 Hz, notch filter 50 Hz, sensitivity 70 micro volts/mm, and a sampling rate of 256 Hz were used for the basic signal processing. Eight EEG electrodes (C3, C4, P3, P4, F3, F4, T3, and T4) were placed according to the international standard 10-20 system of electrode placementOPENML
Contains the forest cover type for 30 x 30 meter cells obtained from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. It contains 581,012 instances and 54 attributes, and it has been used in several papers on data stream classification.OPENML
I had a list of what the 30 or so variables were, but a.) I lost it, and b.), I would not know which 13 variables are included in the set. - The attributes are: 1) Alcohol 2) Malic acid 3) Ash 4) Alcalinity of ash 5) Magnesium 6) Total phenols 7) Flavanoids 8) Nonflavanoid phenols 9) Proanthocyanins 10) Color intensity 11) Hue 12) OD280/OD315OPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning. OPENMLHELPCONTACTPLEASE CITE USOPENML APISSTUDYTASK OpenML is a place where you can share interesting datasets with the people who love to analyse data, and build the best solutions together, saving you valuable time, increasing your visibility, and speeding up discovery. OpenML links data to algorithms and people, so you can build on the state of the art and learn to teach machines tolearn better.
OPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.OPENML
A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University, 2003. #Dataset from the LIBSVM data repository Preprocessing: Original data: an astroparticle application from Jan Conrad of Uppsala University,Sweden.
OPENML
Second quartile (Median) of standard deviation of attributes of the numeric type. Number of attributes divided by the number of instances. Maximum mutual information between the nominal attributes and the target attribute. The minimal number of distinct values among attributes of the nominal type.OPENML
The buildings differ with respect to the glazing area, the glazing area distribution, and the orientation, amongst other parameters. We simulate various settings as functions of the afore-mentioned characteristics to obtain 768 building shapes. The dataset comprises 768 samples and 8 features, aiming to predict two real valuedresponses.
OPENMLCHEAP PERENNIAL PLANTS FOR SALEMARGINAL PLANTS FOR PONDSMARGINAL POND PLANTS FOR SALEMARGINAL WATER PLANTS FOR PONDS The Leaves were collected in the Royal Botanic Gardens, Kew, UK. email: james.cope@kingston.ac.uk (b) This dataset consists of work carried out by James Cope, Charles Mallah, and James Orwell. Donor of database Charles Mallah: charles.mallah@kingston.ac.uk; James Cope: james.cope@kingston.ac.uk ``` ### Dataset Information The originaldata
OPENML
The data is generated from a synthetic data generator Mulcross (see Paper) and available. Mulcross generates a multi-variate normal distribution with a selectable number of anomaly clusters. In our experiments, the basic setting for Mulcross is as following: contamination ratio = 10% (number of anomalies over the total numberof points
OPENML
Second quartile (Median) of mutual information between the nominal attributes and the target attribute. Percentage of instances belonging to the most frequent class. Mean standard deviation of attributes of the numeric type. Second quartile (Median) of skewness among attributes of the numeric type.OPENML
The first 21 features (columns 2-22) are kinematic properties measured by the particle detectors in the accelerator. The last seven features are functions of the first 21 features; these are high-level features derived by physicists to help discriminate between the two classes. There is an interest in using deep learning methods to obviate theOPENML
The dataset represents 88588 sensor data samples collected from the accelerometer and gyroscope from iPhone 5c in 10 seconds intervals and ~5.4/second frequency. ### Attribute information This data is represented by following columns (each column contains sensor data for one of the sensor's axes): acceleration_x acceleration_yacceleration_z
OPENMLHELPCONTACTPLEASE CITE USOPENML APISSTUDYTASK OpenML is a place where you can share interesting datasets with the people who love to analyse data, and build the best solutions together, saving you valuable time, increasing your visibility, and speeding up discovery. OpenML links data to algorithms and people, so you can build on the state of the art and learn to teach machines tolearn better.
OPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.OPENML
A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University, 2003. #Dataset from the LIBSVM data repository Preprocessing: Original data: an astroparticle application from Jan Conrad of Uppsala University,Sweden.
OPENML
Second quartile (Median) of standard deviation of attributes of the numeric type. Number of attributes divided by the number of instances. Maximum mutual information between the nominal attributes and the target attribute. The minimal number of distinct values among attributes of the nominal type.OPENML
The buildings differ with respect to the glazing area, the glazing area distribution, and the orientation, amongst other parameters. We simulate various settings as functions of the afore-mentioned characteristics to obtain 768 building shapes. The dataset comprises 768 samples and 8 features, aiming to predict two real valuedresponses.
OPENMLCHEAP PERENNIAL PLANTS FOR SALEMARGINAL PLANTS FOR PONDSMARGINAL POND PLANTS FOR SALEMARGINAL WATER PLANTS FOR PONDS The Leaves were collected in the Royal Botanic Gardens, Kew, UK. email: james.cope@kingston.ac.uk (b) This dataset consists of work carried out by James Cope, Charles Mallah, and James Orwell. Donor of database Charles Mallah: charles.mallah@kingston.ac.uk; James Cope: james.cope@kingston.ac.uk ``` ### Dataset Information The originaldata
OPENML
The data is generated from a synthetic data generator Mulcross (see Paper) and available. Mulcross generates a multi-variate normal distribution with a selectable number of anomaly clusters. In our experiments, the basic setting for Mulcross is as following: contamination ratio = 10% (number of anomalies over the total numberof points
OPENML
Second quartile (Median) of mutual information between the nominal attributes and the target attribute. Percentage of instances belonging to the most frequent class. Mean standard deviation of attributes of the numeric type. Second quartile (Median) of skewness among attributes of the numeric type.OPENML
The first 21 features (columns 2-22) are kinematic properties measured by the particle detectors in the accelerator. The last seven features are functions of the first 21 features; these are high-level features derived by physicists to help discriminate between the two classes. There is an interest in using deep learning methods to obviate theOPENML
The dataset represents 88588 sensor data samples collected from the accelerometer and gyroscope from iPhone 5c in 10 seconds intervals and ~5.4/second frequency. ### Attribute information This data is represented by following columns (each column contains sensor data for one of the sensor's axes): acceleration_x acceleration_yacceleration_z
OPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.GET STARTED
An open, collaborative, frictionless, automated machine learning environment. Data sets automatically analyzed, annotated, and organized online. Machine learning pipelines automatically shared from many libraries.. Extensive APIs to integrate OpenML into your own tools and scripts. Reproducible results (e.g. models, evaluations) for easy comparison and reuseOPENML
This is the large soybean database from the UCI repository, with its training and test database combined into a single file. There are 19 classes, only the first 15 of which have been used in prior. 40719 runs1 likes53 downloads54 reach13 impact. 683 instances - 36 features - 19 classes - 2337 missing values.OPENML
OpenML Benchmarking Suites and the OpenML-CC18. We advocate the use of curated, comprehensive benchmark suites of machine learning datasets, backed by standardized OpenML-based interfaces and complementary software toolkits written in Python, Java. 0 datasets, 0 tasks, 0flows, 0 runs.
OPENML
OpenML Benchmarking Suites and the OpenML-CC18. We advocate the use of curated, comprehensive benchmark suites of machine learning datasets, backed by standardized OpenML-based interfaces and complementary software toolkits written in Python, Java and R. We demonstrate how to easily execute comprehensive benchmarking studies using standardizedOPENML
Second quartile (Median) of mutual information between the nominal attributes and the target attribute. Percentage of instances belonging to the most frequent class. Mean standard deviation of attributes of the numeric type. Second quartile (Median) of skewness among attributes of the numeric type.OPENML
The Car Evaluation Database contains examples with the structural information removed, i.e., directly relates CAR to the six input attributes: buying, maint, doors, persons, lug_boot, safety. Because of known underlying concept structure, this database may be particularly useful for testing constructive induction and structurediscovery methods
OPENML
Though briefer than the other documentation files found in this database repository, they should suffice to describe the database, specifically: 1. Source 2. Number and names of attributes (including class names) 3. Types of values that each attribute takes In general, these databases are quite similar and can be characterized somewhat asOPENML
Each card is described using two attributes (suit and rank), for a total of 10 predictive attributes. There is one Class attribute that describes the "Poker Hand". The order of cards is important, which is why there are 480 possible Royal Flush hands as compared to 4 (one for each suit). * Attribute Information: 1) S1 "Suit of card #1" OrdinalOPENML
The original thyroid disease (ann-thyroid) dataset from UCI machine learning repository is a classification dataset, which is suited for training ANNs. It has 3772 training instances and 3428 testing instances. It has 15 categorical and 6 real attributes. The problem is to determine whether a patient referred to the clinic is hypothyroid. OPENMLHELPCONTACTPLEASE CITE USOPENML APISSTUDYTASK Identifying the most appropriate machine learning techniques and using them optimally can be challenging for the best of us. OpenML is a place where you can share interesting datasets with the people who love to analyse data, and build the best solutions together, saving you valuable time, increasing your visibility, and speeding updiscovery.
OPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.OPENML
Author: Angeliki Xifara, Athanasios Tsanas Source: UCI Please cite: Source: The dataset was created by Angeliki Xifara (angxifara @ gmail.com, Civil/Structural Engineer) and was processed by Athanasios Tsanas (tsanasthanasis @ gmail.com, Oxford Centre forOPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.GET STARTED
An open, collaborative, frictionless, automated machine learning environment. Data sets automatically analyzed, annotated, and organized online. Machine learning pipelines automatically shared from many libraries.. Extensive APIs to integrate OpenML into your own tools and scripts. Reproducible results (e.g. models, evaluations) for easy comparison and reuseOPENML
Author: Robert Cattral, Franz Oppacher Source: UCI Please cite: * Abstract: Purpose is to predict poker hands * Source - Creators: Robert Cattral (cattral '@' gmail.com) Franz Oppacher (oppacher '@' scs.carleton.ca) Carleton University, Department of Computer Science Intelligent Systems Research Unit 1125 Colonel By Drive, Ottawa, Ontario, Canada, K1S5B6 * Data Set Information: Each record isOPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning. WEKA - OPENML DOCUMENTATIONSEE MORE ON DOCS.OPENML.ORG OPENMLHELPCONTACTPLEASE CITE USOPENML APISSTUDYTASK Identifying the most appropriate machine learning techniques and using them optimally can be challenging for the best of us. OpenML is a place where you can share interesting datasets with the people who love to analyse data, and build the best solutions together, saving you valuable time, increasing your visibility, and speeding updiscovery.
OPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.OPENML
Author: Angeliki Xifara, Athanasios Tsanas Source: UCI Please cite: Source: The dataset was created by Angeliki Xifara (angxifara @ gmail.com, Civil/Structural Engineer) and was processed by Athanasios Tsanas (tsanasthanasis @ gmail.com, Oxford Centre forOPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.GET STARTED
An open, collaborative, frictionless, automated machine learning environment. Data sets automatically analyzed, annotated, and organized online. Machine learning pipelines automatically shared from many libraries.. Extensive APIs to integrate OpenML into your own tools and scripts. Reproducible results (e.g. models, evaluations) for easy comparison and reuseOPENML
Author: Robert Cattral, Franz Oppacher Source: UCI Please cite: * Abstract: Purpose is to predict poker hands * Source - Creators: Robert Cattral (cattral '@' gmail.com) Franz Oppacher (oppacher '@' scs.carleton.ca) Carleton University, Department of Computer Science Intelligent Systems Research Unit 1125 Colonel By Drive, Ottawa, Ontario, Canada, K1S5B6 * Data Set Information: Each record isOPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning. WEKA - OPENML DOCUMENTATIONSEE MORE ON DOCS.OPENML.ORGGET STARTED
An open, collaborative, frictionless, automated machine learning environment. Data sets automatically analyzed, annotated, and organized online. Machine learning pipelines automatically shared from many libraries.. Extensive APIs to integrate OpenML into your own tools and scripts. Reproducible results (e.g. models, evaluations) for easy comparison and reuseOPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.OPENML
We advocate the use of curated, comprehensive benchmark suites of machine learning datasets, backed by standardized OpenML-based interfaces and complementary software toolkits written inOPENML
Cross-validation is a technique to evaluate predictive models by partitioning the original sample into a training set to train the model, and a test set to evaluate it.OPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.OPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.OPENML
Author: Robert Cattral, Franz Oppacher Source: UCI Please cite: * Abstract: Purpose is to predict poker hands * Source - Creators: Robert Cattral (cattral '@' gmail.com) Franz Oppacher (oppacher '@' scs.carleton.ca) Carleton University, Department of Computer Science Intelligent Systems Research Unit 1125 Colonel By Drive, Ottawa, Ontario, Canada, K1S5B6 * Data Set Information: Each record is WEKA - OPENML DOCUMENTATION WEKA. OpenML is integrated in the Weka (Waikato Environment for Knowledge Analysis) Experimenter and the Command Line Interface. Installation¶. OpenML is available as a weka extension in the packagemanager:
WWW.OPENML.ORG
10273451 8323 Heinrich Peters 167141 Supervised Classification 15083 sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro)(1) 8170647 add_indicator false 12737 copy true 12737 fill_value null 12737 missing_values NaN 12737 strategy "most_frequent" 12737 verbose0
WWW.OPENML.ORG
397220 2 Joaquin Vanschoren 1943 Supervised Classification predictive_accuracy 1179 weka.SMO_RBFKernel(3) 2021 weka.classifiers.functions.SMO -- -C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V OPENMLHELPCONTACTPLEASE CITE USOPENML APISSTUDYTASK Identifying the most appropriate machine learning techniques and using them optimally can be challenging for the best of us. OpenML is a place where you can share interesting datasets with the people who love to analyse data, and build the best solutions together, saving you valuable time, increasing your visibility, and speeding updiscovery.
OPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.OPENML
Author: Angeliki Xifara, Athanasios Tsanas Source: UCI Please cite: Source: The dataset was created by Angeliki Xifara (angxifara @ gmail.com, Civil/Structural Engineer) and was processed by Athanasios Tsanas (tsanasthanasis @ gmail.com, Oxford Centre forOPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.GET STARTED
An open, collaborative, frictionless, automated machine learning environment. Data sets automatically analyzed, annotated, and organized online. Machine learning pipelines automatically shared from many libraries.. Extensive APIs to integrate OpenML into your own tools and scripts. Reproducible results (e.g. models, evaluations) for easy comparison and reuseOPENML
Author: Robert Cattral, Franz Oppacher Source: UCI Please cite: * Abstract: Purpose is to predict poker hands * Source - Creators: Robert Cattral (cattral '@' gmail.com) Franz Oppacher (oppacher '@' scs.carleton.ca) Carleton University, Department of Computer Science Intelligent Systems Research Unit 1125 Colonel By Drive, Ottawa, Ontario, Canada, K1S5B6 * Data Set Information: Each record isOPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning. WEKA - OPENML DOCUMENTATIONSEE MORE ON DOCS.OPENML.ORG OPENMLHELPCONTACTPLEASE CITE USOPENML APISSTUDYTASK Identifying the most appropriate machine learning techniques and using them optimally can be challenging for the best of us. OpenML is a place where you can share interesting datasets with the people who love to analyse data, and build the best solutions together, saving you valuable time, increasing your visibility, and speeding updiscovery.
OPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.OPENML
Author: Angeliki Xifara, Athanasios Tsanas Source: UCI Please cite: Source: The dataset was created by Angeliki Xifara (angxifara @ gmail.com, Civil/Structural Engineer) and was processed by Athanasios Tsanas (tsanasthanasis @ gmail.com, Oxford Centre forOPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.GET STARTED
An open, collaborative, frictionless, automated machine learning environment. Data sets automatically analyzed, annotated, and organized online. Machine learning pipelines automatically shared from many libraries.. Extensive APIs to integrate OpenML into your own tools and scripts. Reproducible results (e.g. models, evaluations) for easy comparison and reuseOPENML
Author: Robert Cattral, Franz Oppacher Source: UCI Please cite: * Abstract: Purpose is to predict poker hands * Source - Creators: Robert Cattral (cattral '@' gmail.com) Franz Oppacher (oppacher '@' scs.carleton.ca) Carleton University, Department of Computer Science Intelligent Systems Research Unit 1125 Colonel By Drive, Ottawa, Ontario, Canada, K1S5B6 * Data Set Information: Each record isOPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning. WEKA - OPENML DOCUMENTATIONSEE MORE ON DOCS.OPENML.ORGGET STARTED
An open, collaborative, frictionless, automated machine learning environment. Data sets automatically analyzed, annotated, and organized online. Machine learning pipelines automatically shared from many libraries.. Extensive APIs to integrate OpenML into your own tools and scripts. Reproducible results (e.g. models, evaluations) for easy comparison and reuseOPENML
We advocate the use of curated, comprehensive benchmark suites of machine learning datasets, backed by standardized OpenML-based interfaces and complementary software toolkits written inOPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.OPENML
Cross-validation is a technique to evaluate predictive models by partitioning the original sample into a training set to train the model, and a test set to evaluate it.OPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.OPENML
OpenML: exploring machine learning better, together. An open science platform for machine learning.OPENML
Author: Robert Cattral, Franz Oppacher Source: UCI Please cite: * Abstract: Purpose is to predict poker hands * Source - Creators: Robert Cattral (cattral '@' gmail.com) Franz Oppacher (oppacher '@' scs.carleton.ca) Carleton University, Department of Computer Science Intelligent Systems Research Unit 1125 Colonel By Drive, Ottawa, Ontario, Canada, K1S5B6 * Data Set Information: Each record is WEKA - OPENML DOCUMENTATION WEKA. OpenML is integrated in the Weka (Waikato Environment for Knowledge Analysis) Experimenter and the Command Line Interface. Installation¶. OpenML is available as a weka extension in the packagemanager:
WWW.OPENML.ORG
397220 2 Joaquin Vanschoren 1943 Supervised Classification predictive_accuracy 1179 weka.SMO_RBFKernel(3) 2021 weka.classifiers.functions.SMO -- -C 1.0 -L 0.001 -P 1.0E-12 -N 0 -VWWW.OPENML.ORG
10273451 8323 Heinrich Peters 167141 Supervised Classification 15083 sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro)(1) 8170647 add_indicator false 12737 copy true 12737 fill_value null 12737 missing_values NaN 12737 strategy "most_frequent" 12737 verbose0
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DEMOCRATIZING MACHINE LEARNING As machine learning is enhancing our ability to understand nature and build a better future, it is crucial that we make it transparent and easily accessible to everyone in research, education and industry. The Open Machine Learning project is an inclusive movement to build an open, organized, online ecosystem for machine learning. We build open source tools to discover (and share) open data from any domain , easily draw them into your favourite machine learning environments , quickly build models alongside (and together with) thousands of other data scientists, analyse your results against the state of the art, and even get automatic advice on how to build better models. Stand on the shoulders of giants and make the world a better place. __ Watch the 1-minute introduction.Sign me up!
Signing up is free and brings you lots of powerful features. All public data is always openly available. MACHINE LEARNING __ OPEN SCIENCE Identifying the most appropriate machine learning techniques and using them optimally can be challenging for the best of us. OpenML is a place where you can share interesting datasets with the people who love to analyse data, and build the best solutions together, saving you valuable time, increasing your visibility, and speeding up discovery. OpenML links data to algorithms and people, so you can build on the state of the art and learn to teach machines to learnbetter.
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... AND WHY YOU WANT TO ANALYZE IT Tell people which learning problem you want to solve (e.g., classify observations) by creating _tasks_ describing your goals. This allows meaningful collaboration, easy benchmarking or different methods, and direct comparison to the state of the art. OpenML tasks are machine-readable, allowing tools to automatically get the data and train and evaluate models, so that you can focus on the science.Learn more
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OpenML integrates seamlessly into existing data science environments, so you can readily use it. With a few lines of code or a few clicks, you can import datasets, build algorithms locally, upload models, and (at any time) download your and other people's workflows, models and evaluations for reuse and further analysis. OpenML is directly integrated into the most popular machine learning tools, but you can also build your own integrations with the Python, R, Java, and C++ APIs, or program against the REST API.OpenML APIs >
OpenML integrations > REPRODUCIBLE, REUSABLE, TRANSPARENT RESEARCH The OpenML integrations make sure that all uploaded results are linked to the exact (versions) of datasets, workflows, software, and the people involved. We generate predictions locally using exact procedures, and evaluate them server-side so that results are directly comparable and reusable in further work. Wherever possible, we extract clear descriptions of machine learning workflows and models.Learn more
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