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AUTOML IN H2O.AI
AutoML or Automatic Machine Learning is the process of automating algorithm selection, feature generation, hyperparameter tuning, iterative modeling, and model assessment. AutoML make it easy to train and evaluate machine learning models. Automating repetitive tasks allows people to focus on the data and the business problems they aretrying to
DOWNLOADING & INSTALLING H2O Open a terminal window and run the following command to install H2O on the Anaconda Cloud. The H2O version in this command should match the version that you want to download. If you leave the h2o version blank and specify just h2o, then the latest version will be installed. Forexample:
MAKE AI APPS
Run Anywhere. ~10MB static executables for Linux, Windows, OSX, BSD, Solaris on AMD64, 386, ARM, PPC. Run it on a RPi Zero for great good!H2O CLIENTS
H2O Flow¶. H2O Flow is a Web based (GUI) user interface. It allows users to interactively run their H2O machine learning workflows and iteratively improve them. It combines code execution, text, mathematics, plots, and rich media in a single document. Documentation for H2O Flow can be found here. THE H2O-R PACKAGE • H2O This package allows the user to run basic H2O commands using R commands. In order to use it, you must first have H2O running. To run H2O on your local machine, call h2o.init without any arguments, and H2O will be automatically launched at localhost:54321, where the IP is “127.0.0.1” and the port is 54321. If H2O is running on a cluster,you
SHUT DOWN H2O INSTANCE Details. This method checks if H2O is running at the specified IP address and port, and if it is, shuts down that H2O instance. Note. Users must call h2o.shutdown explicitly in order to shut down the local H2O instance started by R.SAMPLE_RATE
This option is used to specify the row (x-axis) sampling rate (without replacement). The range is 0.0 to 1.0. In GBM and XGBoost, this value defaults to 1; in DRF, this value defaults to 0.6320000291. Row and column sampling ( sample_rate and col_sample_rate) can improve generalization and lead to lower validation and test set errors.SPARKLING WATER
Spark Submit is for submitting self-contained applications. For more information, refer to the Spark documentation. First, initialize H2O: import org.apache.spark.h2o._ val h2oContext = new H2OContext(sc).start() The Sparkling Water distribution provides several examples of self-contained applications built with SparklingWater.
MISSING_VALUES_HANDLING In H2O, the Deep Learning, GLM, and GAM algorithms will either skip or mean-impute rows with NA values. The GLM and GAM algorithms can also use plug_values, which allows you to specify a single-row frame containing values that will be used to impute missing values of the training/validation frame. These three algorithms default toMeanImputation.
MACHINE LEARNING WITH R AND H2O Consume. H2O AI Hybrid Cloud enables data science teams to quickly share their applications with team members and business users, encouraging company-wide adoption.AUTOML IN H2O.AI
AutoML or Automatic Machine Learning is the process of automating algorithm selection, feature generation, hyperparameter tuning, iterative modeling, and model assessment. AutoML make it easy to train and evaluate machine learning models. Automating repetitive tasks allows people to focus on the data and the business problems they aretrying to
DOWNLOADING & INSTALLING H2O Open a terminal window and run the following command to install H2O on the Anaconda Cloud. The H2O version in this command should match the version that you want to download. If you leave the h2o version blank and specify just h2o, then the latest version will be installed. Forexample:
MAKE AI APPS
Run Anywhere. ~10MB static executables for Linux, Windows, OSX, BSD, Solaris on AMD64, 386, ARM, PPC. Run it on a RPi Zero for great good!H2O CLIENTS
H2O Flow¶. H2O Flow is a Web based (GUI) user interface. It allows users to interactively run their H2O machine learning workflows and iteratively improve them. It combines code execution, text, mathematics, plots, and rich media in a single document. Documentation for H2O Flow can be found here. THE H2O-R PACKAGE • H2O This package allows the user to run basic H2O commands using R commands. In order to use it, you must first have H2O running. To run H2O on your local machine, call h2o.init without any arguments, and H2O will be automatically launched at localhost:54321, where the IP is “127.0.0.1” and the port is 54321. If H2O is running on a cluster,you
SHUT DOWN H2O INSTANCE Details. This method checks if H2O is running at the specified IP address and port, and if it is, shuts down that H2O instance. Note. Users must call h2o.shutdown explicitly in order to shut down the local H2O instance started by R.SAMPLE_RATE
This option is used to specify the row (x-axis) sampling rate (without replacement). The range is 0.0 to 1.0. In GBM and XGBoost, this value defaults to 1; in DRF, this value defaults to 0.6320000291. Row and column sampling ( sample_rate and col_sample_rate) can improve generalization and lead to lower validation and test set errors.SPARKLING WATER
Spark Submit is for submitting self-contained applications. For more information, refer to the Spark documentation. First, initialize H2O: import org.apache.spark.h2o._ val h2oContext = new H2OContext(sc).start() The Sparkling Water distribution provides several examples of self-contained applications built with SparklingWater.
MISSING_VALUES_HANDLING In H2O, the Deep Learning, GLM, and GAM algorithms will either skip or mean-impute rows with NA values. The GLM and GAM algorithms can also use plug_values, which allows you to specify a single-row frame containing values that will be used to impute missing values of the training/validation frame. These three algorithms default toMeanImputation.
H2O4GPU | H2O.AI
H2O4GPU is an open-source collection of GPU solvers created by H2O.ai. It builds on the easy-to-use scikit-learn Python API and its well-tested CPU-based algorithms. It can be used as a drop-in replacement for scikit-learn with support for GPUs on selected (and ever-growing) algorithms.AUTOML IN H2O.AI
AutoML or Automatic Machine Learning is the process of automating algorithm selection, feature generation, hyperparameter tuning, iterative modeling, and model assessment. AutoML make it easy to train and evaluate machine learning models. Automating repetitive tasks allows people to focus on the data and the business problems they aretrying to
FINALLY, YOU CAN PLOT H2O DECISION TREES IN R Finally, there is package data.tree designed specifically to create and analyze trees in R. It fits the bill of representing and visualizing decision trees perfectly, so it became a tool of choice for this post. Still, visualizing H2O model trees could be fully reproduced with any of network and visualization packages mentionedabove.
THE H2O PYTHON MODULE The goal of H2O is to allow simple horizontal scaling to a given problem in order to produce a solution faster. The conceptual paradigm MapReduce (AKA “divide and conquer and combine”), along with a good concurrent application structure, (c.f. jsr166y and NonBlockingHashMap) enable this CATEGORICAL_ENCODING auto or AUTO: Allow the algorithm to decide (default).For GBM, DRF, and Isolation Forest, the algorithm will perform Enum encoding when auto option is specified.. enum or Enum: Leave the dataset as is, internally map the strings to integers, and use these integers to make splits - either via ordinal nature when nbins_cats is too small to resolve all levels or via bitsets that do a perfect XGBOOST — H2O 3.32.1.3 DOCUMENTATION Introduction¶. XGBoost is a supervised learning algorithm that implements a process called boosting to yield accurate models. Boosting refers to the ensemble learning technique of building many models sequentially, with each new model attempting to correct for the deficiencies in the previous model. SUPPORT VECTOR MACHINE (SVM) Introduction¶. In machine learning, support vector machines are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model thatassigns
MTRIES — H2O 3.32.1.3 DOCUMENTATION Use this option to specify the number of columns to randomly select at each level. This value defaults to -1. Valid values for this option are -2, -1, and any value >= 1. If a value other than -1 or -2 is used, then the number of variables is: p/3 for regression (where p is the number of predictors). Note: If mtries=-2, it uses all featuresfor
FOLD_COLUMN
The fold_column option specifies the column in the dataset that contains the cross-validation fold index assignment per observation. The fold column can include integers (0, 1, 2, , N-1 values or 1, 2, 3 , N values) or categorical values. When specified, the algorithm uses SCORE_EACH_ITERATION Description¶. This option allows you to specify to score during each iteration of model training. This option is useful when used with early stopping and attempting to make early stopping reproducible. When used with early stopping, the stopping_rounds option applies to the number of scoring iterations that H2O has performed, so regular DOWNLOADING & INSTALLING H2O Open a terminal window and run the following command to install H2O on the Anaconda Cloud. The H2O version in this command should match the version that you want to download. If you leave the h2o version blank and specify just h2o, then the latest version will be installed. Forexample:
MAKE AI APPS
Run Anywhere. ~10MB static executables for Linux, Windows, OSX, BSD, Solaris on AMD64, 386, ARM, PPC. Run it on a RPi Zero for great good! THE H2O-R PACKAGE • H2O This package allows the user to run basic H2O commands using R commands. In order to use it, you must first have H2O running. To run H2O on your local machine, call h2o.init without any arguments, and H2O will be automatically launched at localhost:54321, where the IP is “127.0.0.1” and the port is 54321. If H2O is running on a cluster,you
H2O CLIENTS
H2O Flow¶. H2O Flow is a Web based (GUI) user interface. It allows users to interactively run their H2O machine learning workflows and iteratively improve them. It combines code execution, text, mathematics, plots, and rich media in a single document. Documentation for H2O Flow can be found here. SHUT DOWN H2O INSTANCE Details. This method checks if H2O is running at the specified IP address and port, and if it is, shuts down that H2O instance. Note. Users must call h2o.shutdown explicitly in order to shut down the local H2O instance started by R.TARGET ENCODING
Target Encoding. Target encoding is the process of replacing a categorical value with the mean of the target variable. Any non-categorical columns are automatically dropped by the target encoder model. Note: You can also use target encoding to convert categorical columns to numeric. This can help improve machine learningaccuracy since
THE H2O PYTHON MODULE The H2O Python Module. This Python module provides access to the H2O JVM, as well as its extensions, objects, machine-learning algorithms, and modeling support capabilities, such as basic munging and feature generation. The H2O JVM provides a web server so that all communication occurs on a socket (specified by an IP address and aport) via a
STANDARDIZE
This option specifies whether to standardizes numeric columns to have zero mean and unit variance. Enabling this option produces standardized coefficient magnitudes in the model output. Standardization is highly recommended. As such, this option is enabled by default. If you do not use standardization, the results can includecomponents that
SCORE_EACH_ITERATION Description¶. This option allows you to specify to score during each iteration of model training. This option is useful when used with early stopping and attempting to make early stopping reproducible. When used with early stopping, the stopping_rounds option applies to the number of scoring iterations that H2O has performed, so regular MISSING_VALUES_HANDLING In H2O, the Deep Learning, GLM, and GAM algorithms will either skip or mean-impute rows with NA values. The GLM and GAM algorithms can also use plug_values, which allows you to specify a single-row frame containing values that will be used to impute missing values of the training/validation frame. These three algorithms default toMeanImputation.
DOWNLOADING & INSTALLING H2O Open a terminal window and run the following command to install H2O on the Anaconda Cloud. The H2O version in this command should match the version that you want to download. If you leave the h2o version blank and specify just h2o, then the latest version will be installed. Forexample:
MAKE AI APPS
Run Anywhere. ~10MB static executables for Linux, Windows, OSX, BSD, Solaris on AMD64, 386, ARM, PPC. Run it on a RPi Zero for great good! THE H2O-R PACKAGE • H2O This package allows the user to run basic H2O commands using R commands. In order to use it, you must first have H2O running. To run H2O on your local machine, call h2o.init without any arguments, and H2O will be automatically launched at localhost:54321, where the IP is “127.0.0.1” and the port is 54321. If H2O is running on a cluster,you
H2O CLIENTS
H2O Flow¶. H2O Flow is a Web based (GUI) user interface. It allows users to interactively run their H2O machine learning workflows and iteratively improve them. It combines code execution, text, mathematics, plots, and rich media in a single document. Documentation for H2O Flow can be found here. SHUT DOWN H2O INSTANCE Details. This method checks if H2O is running at the specified IP address and port, and if it is, shuts down that H2O instance. Note. Users must call h2o.shutdown explicitly in order to shut down the local H2O instance started by R.TARGET ENCODING
Target Encoding. Target encoding is the process of replacing a categorical value with the mean of the target variable. Any non-categorical columns are automatically dropped by the target encoder model. Note: You can also use target encoding to convert categorical columns to numeric. This can help improve machine learningaccuracy since
THE H2O PYTHON MODULE The H2O Python Module. This Python module provides access to the H2O JVM, as well as its extensions, objects, machine-learning algorithms, and modeling support capabilities, such as basic munging and feature generation. The H2O JVM provides a web server so that all communication occurs on a socket (specified by an IP address and aport) via a
STANDARDIZE
This option specifies whether to standardizes numeric columns to have zero mean and unit variance. Enabling this option produces standardized coefficient magnitudes in the model output. Standardization is highly recommended. As such, this option is enabled by default. If you do not use standardization, the results can includecomponents that
SCORE_EACH_ITERATION Description¶. This option allows you to specify to score during each iteration of model training. This option is useful when used with early stopping and attempting to make early stopping reproducible. When used with early stopping, the stopping_rounds option applies to the number of scoring iterations that H2O has performed, so regular MISSING_VALUES_HANDLING In H2O, the Deep Learning, GLM, and GAM algorithms will either skip or mean-impute rows with NA values. The GLM and GAM algorithms can also use plug_values, which allows you to specify a single-row frame containing values that will be used to impute missing values of the training/validation frame. These three algorithms default toMeanImputation.
AUTOML IN H2O.AI
AutoML or Automatic Machine Learning is the process of automating algorithm selection, feature generation, hyperparameter tuning, iterative modeling, and model assessment. AutoML make it easy to train and evaluate machine learning models. Automating repetitive tasks allows people to focus on the data and the business problems they aretrying to
FINALLY, YOU CAN PLOT H2O DECISION TREES IN R Finally, there is package data.tree designed specifically to create and analyze trees in R. It fits the bill of representing and visualizing decision trees perfectly, so it became a tool of choice for this post. Still, visualizing H2O model trees could be fully reproduced with any of network and visualization packages mentionedabove.
MODEL OBJECT, OPTIMIZED (MOJO) MOJO (stands for Model Object, Optimized) is a standalone, low-latency model object designed to be easily embeddable in production environments. In Driverless AI, the MOJO Model is combined with a feature engineering pipeline to create a MOJO scoring pipeline that can be executed in XGBOOST — H2O 3.32.1.3 DOCUMENTATION Defining an XGBoost Model¶. model_id: (Optional) Specify a custom name for the model to use as a reference.By default, H2O automatically generates a destination key. training_frame: (Required) Specify the dataset used to build the model.NOTE: In Flow, if you click the Build a model button from the Parse cell, the training frame is enteredautomatically.
WORD2VEC — H2O 3.32.1.3 DOCUMENTATION Defining a Word2vec Model¶. model_id: (Optional) Specify a custom name for the model to use as a reference.By default, H2O automatically generates a destination key. training_frame: (Required) Specify the dataset used to build the model.The training_frame should be a single column H2OFrame that is composed of the tokenized text. (Refer to Tokenize Strings in the Data Manipulation section forVARIABLE IMPORTANCE
Variable Importance¶. This section describes how variable importance is calculated for tree-based models. For examples, this section uses the cars dataset to classify whether or not a car is fuel efficient based on its weight and the year it was built.SAMPLE_RATE
This option is used to specify the row (x-axis) sampling rate (without replacement). The range is 0.0 to 1.0. In GBM and XGBoost, this value defaults to 1; in DRF, this value defaults to 0.6320000291. Row and column sampling ( sample_rate and col_sample_rate) can improve generalization and lead to lower validation and test set errors. MISSING_VALUES_HANDLING In H2O, the Deep Learning, GLM, and GAM algorithms will either skip or mean-impute rows with NA values. The GLM and GAM algorithms can also use plug_values, which allows you to specify a single-row frame containing values that will be used to impute missing values of the training/validation frame. These three algorithms default toMeanImputation.
SCORE_EACH_ITERATION Description¶. This option allows you to specify to score during each iteration of model training. This option is useful when used with early stopping and attempting to make early stopping reproducible. When used with early stopping, the stopping_rounds option applies to the number of scoring iterations that H2O has performed, so regular MTRIES — H2O 3.32.1.3 DOCUMENTATION Use this option to specify the number of columns to randomly select at each level. This value defaults to -1. Valid values for this option are -2, -1, and any value >= 1. If a value other than -1 or -2 is used, then the number of variables is: p/3 for regression (where p is the number of predictors). Note: If mtries=-2, it uses all featuresfor
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PAYROLL ANALYSIS
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TRANSACTION FORECASTING AND ANOMALY DETECTION Two ways for mobile transaction forecasting & anomaly detection.Watch the Video
CONSUMER CHURN
Paypal solves customer churn challenges.Watch the Video
AI POWERED AUDIT
PwC combining deep dynamic machine analysis with the human experience to pinpoint errors, decrease risks and find fraud in near real time.Watch the Video
INTERPRETABLE MACHINE LEARNINGWatch the Video
INSIGHTS TO MAKE BETTER CREDIT DECISIONS Capital One evaluates large data sets to help customers make bettercredit decisions.
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MINIMIZING RISK
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ANTI MONEY LAUNDERING Intercepting dirty money using machine learning.Watch the Video
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ADP created actionable data analysis tools for payroll clients.Watch the Video
TRANSACTION FORECASTING AND ANOMALY DETECTION Two ways for mobile transaction forecasting & anomaly detection.Watch the Video
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Paypal solves customer churn challenges.Watch the Video
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