Reference

apply()

Apply on H2O Datasets

as.character(<H2OFrame>)

Convert an H2OFrame to a String

as.data.frame(<H2OFrame>)

Converts parsed H2O data into an R data frame

as.data.frame(<H2OSegmentModels>)

Converts a collection of Segment Models to a data.frame

as.factor()

Convert H2O Data to Factors

as.h2o()

Create H2OFrame

as.matrix(<H2OFrame>)

Convert an H2OFrame to a matrix

as.numeric()

Convert H2O Data to Numeric

as.vector(<H2OFrame>)

Convert an H2OFrame to a vector

australia

Australia Coastal Data

colnames()

Returns the column names of an H2OFrame

dim(<H2OFrame>)

Returns the Dimensions of an H2OFrame

dimnames(<H2OFrame>)

Column names of an H2OFrame

feature_frequencies.H2OModel() h2o.feature_frequencies()

Retrieve the number of occurrences of each feature for given observations Available for GBM, Random Forest and Isolation Forest models.

get_seed.H2OModel() h2o.get_seed()

Get the seed from H2OModel which was used during training. If a user does not set the seed parameter before training, the seed is autogenerated. It returns seed as the string if the value is bigger than the integer. For example, an autogenerated seed is always long so that the seed in R is a string.

h2o-package

H2O R Interface

h2o.abs()

Compute the absolute value of x

h2o.acos()

Compute the arc cosine of x

h2o.adaBoost()

Build an AdaBoost model

h2o.aggregated_frame()

Retrieve an aggregated frame from an Aggregator model

h2o.aggregator()

Build an Aggregated Frame

h2o.aic()

Retrieve the Akaike information criterion (AIC) value

h2o.all()

Given a set of logical vectors, are all of the values true?

h2o.anomaly()

Anomaly Detection via H2O Deep Learning Model

h2o.any()

Given a set of logical vectors, is at least one of the values true?

h2o.anyFactor()

Check H2OFrame columns for factors

h2o.api()

Perform a REST API request to a previously connected server.

h2o.arrange()

Sorts an H2O frame by columns

h2o.as_date()

Convert between character representations and objects of Date class

h2o.ascharacter()

Convert H2O Data to Characters

h2o.asfactor()

Convert H2O Data to Factors

h2o.asnumeric()

Convert H2O Data to Numerics

h2o.assign()

Rename an H2O object.

h2o.auc()

Retrieve the AUC

h2o.automl()

Automatic Machine Learning

h2o.auuc()

Retrieve AUUC

h2o.average_objective()

Extracts the final training average objective function of a GLM model.

h2o.betweenss()

Get the between cluster sum of squares

h2o.biases()

Return the respective bias vector

h2o.bottomN()

H2O bottomN

h2o.cbind()

Combine H2O Datasets by Columns

h2o.ceiling()

Take a single numeric argument and return a numeric vector with the smallest integers

h2o.centers()

Retrieve the Model Centers

h2o.centersSTD()

Retrieve the Model Centers STD

h2o.centroid_stats()

Retrieve centroid statistics

h2o.clearLog()

Delete All H2O R Logs

h2o.cluster_sizes()

Retrieve the cluster sizes

h2o.clusterInfo()

Print H2O cluster info

h2o.clusterIsUp()

Determine if an H2O cluster is up or not

h2o.clusterStatus()

Return the status of the cluster

h2o.coef()

Return the coefficients that can be applied to the non-standardized data.

h2o.coef_norm()

Return coefficients fitted on the standardized data (requires standardize = True, which is on by default). These coefficients can be used to evaluate variable importance.

h2o.coef_with_p_values()

Return the coefficients table with coefficients, standardized coefficients, p-values, z-values and std-error for GLM models

h2o.colnames()

Return column names of an H2OFrame

h2o.columns_by_type()

Obtain a list of columns that are specified by `coltype`

h2o.computeGram()

Compute weighted gram matrix.

h2o.confusionMatrix()

Access H2O Confusion Matrices

h2o.connect()

Connect to a running H2O instance.

h2o.cor() cor()

Correlation of columns.

h2o.cos()

Compute the cosine of x

h2o.cosh()

Compute the hyperbolic cosine of x

h2o.coxph()

Trains a Cox Proportional Hazards Model (CoxPH) on an H2O dataset

h2o.createFrame()

Data H2OFrame Creation in H2O

h2o.cross_validation_fold_assignment()

Retrieve the cross-validation fold assignment

h2o.cross_validation_holdout_predictions()

Retrieve the cross-validation holdout predictions

h2o.cross_validation_models()

Retrieve the cross-validation models

h2o.cross_validation_predictions()

Retrieve the cross-validation predictions

h2o.cummax()

Return the cumulative max over a column or across a row

h2o.cummin()

Return the cumulative min over a column or across a row

h2o.cumprod()

Return the cumulative product over a column or across a row

h2o.cumsum()

Return the cumulative sum over a column or across a row

h2o.cut() cut(<H2OFrame>)

Cut H2O Numeric Data to Factor

h2o.day() day()

Convert Milliseconds to Day of Month in H2O Datasets

h2o.dayOfWeek() dayOfWeek()

Convert Milliseconds to Day of Week in H2O Datasets

h2o.dct()

Compute DCT of an H2OFrame

h2o.ddply()

Split H2O Dataset, Apply Function, and Return Results

h2o.decision_tree()

Build a Decision Tree model

h2o.decryptionSetup()

Setup a Decryption Tool

h2o.deepfeatures()

Feature Generation via H2O Deep Learning

h2o.deeplearning()

Build a Deep Neural Network model using CPUs

h2o.describe()

H2O Description of A Dataset

h2o.difflag1()

Conduct a lag 1 transform on a numeric H2OFrame column

h2o.dim()

Returns the number of rows and columns for an H2OFrame object.

h2o.dimnames()

Column names of an H2OFrame

h2o.distance()

Compute a pairwise distance measure between all rows of two numeric H2OFrames.

h2o.download_model()

Download the model in binary format. The owner of the file saved is the user by which python session was executed.

h2o.download_mojo()

Download the model in MOJO format.

h2o.download_pojo()

Download the Scoring POJO (Plain Old Java Object) of an H2O Model

h2o.downloadAllLogs()

Download H2O Log Files to Disk

h2o.downloadCSV()

Download H2O Data to Disk

h2o.drop_duplicates()

Drops duplicated rows.

h2o.entropy()

Shannon entropy

h2o.exp()

Compute the exponential function of x

h2o.explain()

Generate Model Explanations

h2o.explain_row()

Generate Model Explanations for a single row

h2o.exportFile()

Export an H2O Data Frame (H2OFrame) to a File or to a collection of Files.

h2o.exportHDFS()

Export a Model to HDFS

h2o.feature_interaction()

Feature interactions and importance, leaf statistics and split value histograms in a tabular form. Available for XGBoost and GBM.

h2o.fillna()

fillNA

h2o.filterNACols()

Filter NA Columns

h2o.find_row_by_threshold()

Find the threshold, give the max metric. No duplicate thresholds allowed

h2o.find_threshold_by_max_metric()

Find the threshold, give the max metric

h2o.findSynonyms()

Find synonyms using a word2vec model.

h2o.floor()

Take a single numeric argument and return a numeric vector with the largest integers

h2o.flow()

Open H2O Flow

h2o.gainsLift() h2o.gains_lift()

Access H2O Gains/Lift Tables

h2o.gains_lift_plot()

Plot Gains/Lift curves

h2o.gbm()

Build gradient boosted classification or regression trees

h2o.generic()

Imports a generic model into H2O. Such model can be used then used for scoring and obtaining additional information about the model. The imported model has to be supported by H2O.

h2o.genericModel()

Imports a model under given path, creating a Generic model with it.

h2o.get_automl() h2o.getAutoML()

Get an R object that is a subclass of H2OAutoML

h2o.get_knot_locations()

Extracts the knot locations from model output if it is enabled.

h2o.get_leaderboard()

Retrieve the leaderboard from the AutoML instance.

h2o.get_ntrees_actual()

Retrieve actual number of trees for tree algorithms

h2o.get_segment_models()

Retrieves an instance of H2OSegmentModels for a given id.

h2o.getConnection()

Retrieve an H2O Connection

h2o.getFrame()

Get an R Reference to an H2O Dataset, that will NOT be GC'd by default

h2o.getGLMFullRegularizationPath()

Extract full regularization path from a GLM model

h2o.getGrid()

Get a grid object from H2O distributed K/V store.

h2o.getId()

Get back-end distributed key/value store id from an H2OFrame.

h2o.getModel()

Get an R reference to an H2O model

h2o.getModelTree()

Fetchces a single tree of a H2O model. This function is intended to be used on Gradient Boosting Machine models or Distributed Random Forest models.

h2o.getTimezone()

Get the Time Zone on the H2O cluster Returns a string

h2o.getTypes()

Get the types-per-column

h2o.getVersion()

Get h2o version

h2o.giniCoef()

Retrieve the GINI Coefficcient

h2o.gam()

Fit a General Additive Model

h2o.anovaglm()

H2O ANOVAGLM is used to calculate Type III SS which is used to evaluate the contributions of individual predictors and their interactions to a model. Predictors or interactions with negligible contributions to the model will have high p-values while those with more contributions will have low p-values.

h2o.get_best_r2_values()

Extracts the best R2 values for all predictor subset size.

h2o.get_best_model_predictors()

Extracts the subset of predictor names that yield the best R2 value for each predictor subset size.

h2o.get_gam_knot_column_names()

Extracts the gam column names corresponding to the knot locations from model output if it is enabled.

h2o.get_predictors_added_per_step()

Extracts the predictor added to model at each step.

h2o.get_predictors_removed_per_step()

Extracts the predictor removed to model at each step.

h2o.get_regression_influence_diagnostics()

Extracts a list of H2OFrames containing regression influence diagnostics for predictor subsets of various sizes or just one H2OFrame containing regression influence diagnostics for predictor subsets of one fixed size

h2o.get_variable_inflation_factors()

Return the variable inflation factors associated with numerical predictors for GLM models.

h2o.glm()

Fit a generalized linear model

h2o.glrm()

Generalized low rank decomposition of an H2O data frame

h2o.grep()

Search for matches to an argument pattern

h2o.grid()

Launch grid search with given algorithm and parameters.

h2o.group_by()

Group and Apply by Column

h2o.gsub()

String Global Substitute

h2o.head() head(<H2OFrame>) h2o.tail() tail(<H2OFrame>)

Return the Head or Tail of an H2O Dataset.

h2o.hist()

Compute A Histogram

h2o.hit_ratio_table()

Retrieve the Hit Ratios

h2o.hour() hour()

Convert Milliseconds to Hour of Day in H2O Datasets

h2o.ice_plot()

Plot Individual Conditional Expectation (ICE) for each decile

h2o.ifelse() ifelse()

H2O Apply Conditional Statement

h2o.import_hive_table()

Import Hive Table into H2O

h2o.import_mojo()

Imports a MOJO under given path, creating a Generic model with it.

h2o.import_sql_select()

Import SQL table that is result of SELECT SQL query into H2O

h2o.import_sql_table()

Import SQL Table into H2O

h2o.importFile() h2o.importFolder() h2o.importHDFS() h2o.uploadFile()

Import Files into H2O

h2o.impute()

Basic Imputation of H2O Vectors

h2o.infogram()

H2O Infogram

h2o.init()

Initialize and Connect to H2O

h2o.insertMissingValues()

Insert Missing Values into an H2OFrame

h2o.interaction()

Categorical Interaction Feature Creation in H2O

h2o.is_client()

Check Client Mode Connection

h2o.isax()

iSAX

h2o.ischaracter()

Check if character

h2o.isfactor()

Check if factor

h2o.isnumeric()

Check if numeric

h2o.isolationForest()

Trains an Isolation Forest model

h2o.keyof()

Method on Keyed objects allowing to obtain their key.

h2o.extendedIsolationForest()

Trains an Extended Isolation Forest model

h2o.kfold_column()

Produce a k-fold column vector.

h2o.killMinus3()

Dump the stack into the JVM's stdout.

h2o.kmeans()

Performs k-means clustering on an H2O dataset

h2o.kolmogorov_smirnov()

Kolmogorov-Smirnov metric for binomial models

h2o.kurtosis() kurtosis.H2OFrame()

Kurtosis of a column

h2o.learning_curve_plot()

Learning Curve Plot

h2o.levels()

Return the levels from the column requested column.

h2o.list_all_extensions()

List all H2O registered extensions

h2o.list_api_extensions()

List registered API extensions

h2o.list_core_extensions()

List registered core extensions

h2o.list_jobs()

Return list of jobs performed by the H2O cluster

h2o.list_models()

Get an list of all model ids present in the cluster

h2o.listTimezones()

List all of the Time Zones Acceptable by the H2O cluster.

h2o.loadGrid()

Loads previously saved grid with all it's models from the same folder

h2o.loadModel()

Load H2O Model from HDFS or Local Disk

h2o.load_frame()

Load frame previously stored in H2O's native format.

h2o.log()

Compute the logarithm of x

h2o.log1p()

Compute the log1p of x

h2o.log2()

Compute the log2 of x

h2o.log10()

Compute the log10 of x

h2o.logAndEcho()

Log a message on the server-side logs

h2o.loglikelihood()

Retrieve the log likelihood value

h2o.logloss()

Retrieve the Log Loss Value

h2o.ls()

List Keys on an H2O Cluster

h2o.lstrip()

Strip set from left

h2o.mae()

Retrieve the Mean Absolute Error Value

h2o.make_metrics()

Create Model Metrics from predicted and actual values in H2O

h2o.makeGLMModel()

Set betas of an existing H2O GLM Model

h2o.match() match.H2OFrame() `%in%`

Value Matching in H2O

h2o.max()

Returns the maxima of the input values.

h2o.modelSelection()

H2O ModelSelection is used to build the best model with one predictor, two predictors, ... up to max_predictor_number specified in the algorithm parameters when mode=allsubsets. The best model is the one with the highest R2 value. When mode=maxr, the model returned is no longer guaranteed to have the best R2 value.

h2o.mean_per_class_error()

Retrieve the mean per class error

h2o.mean_residual_deviance()

Retrieve the Mean Residual Deviance value

h2o.mean() mean(<H2OFrame>)

Compute the frame's mean by-column (or by-row).

h2o.median() median(<H2OFrame>)

H2O Median

h2o.melt()

Converts a frame to key-value representation while optionally skipping NA values. Inverse operation to h2o.pivot.

h2o.merge()

Merge Two H2O Data Frames

h2o.metric() h2o.F0point5() h2o.F1() h2o.F2() h2o.accuracy() h2o.error() h2o.maxPerClassError() h2o.mean_per_class_accuracy() h2o.mcc() h2o.precision() h2o.tpr() h2o.fpr() h2o.fnr() h2o.tnr() h2o.recall() h2o.sensitivity() h2o.fallout() h2o.missrate() h2o.specificity()

H2O Model Metric Accessor Functions

h2o.min()

Returns the minima of the input values.

h2o.mktime()

Compute msec since the Unix Epoch

h2o.model_correlation_heatmap()

Model Prediction Correlation Heatmap

h2o.mojo_predict_csv()

H2O Prediction from R without having H2O running

h2o.mojo_predict_df()

H2O Prediction from R without having H2O running

h2o.month() month()

Convert Milliseconds to Months in H2O Datasets

h2o.mse()

Retrieves Mean Squared Error Value

h2o.na_omit()

Remove Rows With NAs

h2o.nacnt()

Count of NAs per column

h2o.naiveBayes()

Compute naive Bayes probabilities on an H2O dataset.

h2o.names()

Column names of an H2OFrame

h2o.nchar()

String length

h2o.ncol()

Return the number of columns present in x.

h2o.negative_log_likelihood()

Extracts the final training negative log likelihood of a GLM model.

h2o.networkTest()

View Network Traffic Speed

h2o.nlevels()

Get the number of factor levels for this frame.

h2o.no_progress()

Disable Progress Bar

h2o.nrow()

Return the number of rows present in x.

h2o.null_deviance()

Retrieve the null deviance

h2o.null_dof()

Retrieve the null degrees of freedom

h2o.num_iterations()

Retrieve the number of iterations.

h2o.num_valid_substrings()

Count of substrings >= 2 chars that are contained in file

h2o.openLog()

View H2O R Logs

h2o.pareto_front()

Plot Pareto front

h2o.parseRaw()

H2O Data Parsing

h2o.parseSetup()

Get a parse setup back for the staged data.

h2o.partialPlot()

Partial Dependence Plots

h2o.pd_multi_plot()

Plot partial dependencies for a variable across multiple models

h2o.pd_plot()

Plot partial dependence for a variable

h2o.performance()

Model Performance Metrics in H2O

h2o.permutation_importance()

Calculate Permutation Feature Importance.

h2o.permutation_importance_plot()

Plot Permutation Variable Importances.

h2o.pivot()

Pivot a frame

h2o.prcomp()

Principal component analysis of an H2O data frame

h2o.predict()

Predict on an H2O Model

h2o.predict_json()

H2O Prediction from R without having H2O running

h2o.print()

Print An H2OFrame

h2o.prod()

Return the product of all the values present in its arguments.

h2o.proj_archetypes()

Convert Archetypes to Features from H2O GLRM Model

h2o.aucpr() h2o.pr_auc()

Retrieve the AUCPR (Area Under Precision Recall Curve)

h2o.psvm()

Trains a Support Vector Machine model on an H2O dataset

h2o.quantile() quantile(<H2OFrame>)

Quantiles of H2O Frames.

h2o.r2()

Retrieve the R2 value

h2o.randomForest()

Build a Random Forest model

h2o.range()

Returns a vector containing the minimum and maximum of all the given arguments.

h2o.rank_within_group_by()

This function will add a new column rank where the ranking is produced as follows: 1. sorts the H2OFrame by columns sorted in by columns specified in group_by_cols and sort_cols in the directions specified by the ascending for the sort_cols. The sort directions for the group_by_cols are ascending only. 2. A new rank column is added to the frame which will contain a rank assignment performed next. The user can choose to assign a name to this new column. The default name is New_Rank_column. 3. For each groupby groups, a rank is assigned to the row starting from 1, 2, ... to the end of that group. 4. If sort_cols_sorted is TRUE, a final sort on the frame will be performed frame according to the sort_cols and the sort directions in ascending. If sort_cols_sorted is FALSE (by default), the frame from step 3 will be returned as is with no extra sort. This may provide a small speedup if desired.

h2o.rbind()

Combine H2O Datasets by Rows

h2o.reconstruct()

Reconstruct Training Data via H2O GLRM Model

h2o.relevel()

Reorders levels of an H2O factor, similarly to standard R's relevel.

h2o.removeAll()

Remove All Objects on the H2O Cluster

h2o.removeVecs()

Delete Columns from an H2OFrame

h2o.rep_len()

Replicate Elements of Vectors or Lists into H2O

h2o.reset_threshold()

Reset model threshold and return old threshold value.

h2o.residual_analysis_plot()

Residual Analysis

h2o.residual_deviance()

Retrieve the residual deviance

h2o.residual_dof()

Retrieve the residual degrees of freedom

h2o.result()

Retrieve the results to view the best predictor subsets.

h2o.rm()

Delete Objects In H2O

h2o.rmse()

Retrieves Root Mean Squared Error Value

h2o.rmsle()

Retrieve the Root Mean Squared Log Error

h2o.round() round()

Round doubles/floats to the given number of decimal places.

h2o.rstrip()

Strip set from right

h2o.runif()

Produce a Vector of Random Uniform Numbers

h2o.save_mojo()

Save an H2O Model Object as Mojo to Disk

h2o.save_to_hive()

Save contents of this data frame into a Hive table

h2o.saveGrid()

Saves an existing Grid of models into a given folder.

h2o.saveModel()

Save an H2O Model Object to Disk

h2o.saveModelDetails()

Save an H2O Model Details

h2o.saveMojo()

Deprecated - use h2o.save_mojo instead. Save an H2O Model Object as Mojo to Disk

h2o.save_frame()

Store frame data in H2O's native format.

h2o.scale()

Scaling and Centering of an H2OFrame

h2o.scoreHistory()

Retrieve Model Score History

h2o.scoreHistoryGAM()

Retrieve GLM Model Score History buried in GAM model

h2o.sd() sd()

Standard Deviation of a column of data.

h2o.sdev()

Retrieve the standard deviations of principal components

h2o.set_s3_credentials()

Creates a new Amazon S3 client internally with specified credentials.

h2o.setLevels()

Set Levels of H2O Factor Column

h2o.setTimezone()

Set the Time Zone on the H2O cluster

h2o.shap_explain_row_plot()

SHAP Local Explanation

h2o.shap_summary_plot()

SHAP Summary Plot

h2o.show_progress()

Enable Progress Bar

h2o.shutdown()

Shut Down H2O Instance

h2o.signif() signif()

Round doubles/floats to the given number of significant digits.

h2o.sin()

Compute the sine of x

h2o.skewness() skewness.H2OFrame()

Skewness of a column

h2o.splitFrame()

Split an H2O Data Set

h2o.sqrt()

Compute the square root of x

h2o.stackedEnsemble()

Builds a Stacked Ensemble

h2o.startLogging()

Start Writing H2O R Logs

h2o.std_coef_plot()

Plot Standardized Coefficient Magnitudes

h2o.stopLogging()

Stop Writing H2O R Logs

h2o.str()

Display the structure of an H2OFrame object

h2o.stringdist()

Compute element-wise string distances between two H2OFrames

h2o.strsplit()

String Split

h2o.sub()

String Substitute

h2o.substring() h2o.substr()

Substring

h2o.sum()

Compute the frame's sum by-column (or by-row).

h2o.summary() summary(<H2OFrame>)

Summarizes the columns of an H2OFrame.

h2o.svd()

Singular value decomposition of an H2O data frame using the power method

h2o.table() table.H2OFrame()

Cross Tabulation and Table Creation in H2O

h2o.tabulate()

Tabulation between Two Columns of an H2OFrame

h2o.tan()

Compute the tangent of x

h2o.tanh()

Compute the hyperbolic tangent of x

h2o.target_encode_apply()

Apply Target Encoding Map to Frame

h2o.target_encode_create()

Create Target Encoding Map

h2o.targetencoder()

Transformation of a categorical variable with a mean value of the target variable

h2o.tf_idf()

Computes TF-IDF values for each word in given documents.

h2o.toFrame()

Convert a word2vec model into an H2OFrame

h2o.tokenize()

Tokenize String

h2o.tolower()

Convert strings to lowercase

h2o.topN()

H2O topN

h2o.topBottomN()

H2O topBottomN

h2o.tot_withinss()

Get the total within cluster sum of squares.

h2o.totss()

Get the total sum of squares.

h2o.toupper()

Convert strings to uppercase

h2o.train_segments()

Start Segmented-Data bulk Model Training for a given algorithm and parameters.

h2o.transform()

Use H2O Transformation model and apply the underlying transformation

h2o.transform_frame()

Use GRLM to transform a frame.

h2o.transform_word2vec()

Transform words (or sequences of words) to vectors using a word2vec model.

h2o.transform(<H2OTargetEncoderModel>)

Applies target encoding to a given dataset

h2o.transform(<H2OWordEmbeddingModel>)

Transform words (or sequences of words) to vectors using a word2vec model.

h2o.trim()

Trim Space

h2o.trunc()

Truncate values in x toward 0

h2o.unique()

H2O Unique

h2o.upliftRandomForest()

Build a Uplift Random Forest model

h2o.upload_model()

Upload a binary model from the provided local path to the H2O cluster. (H2O model can be saved in a binary form either by saveModel() or by download_model() function.)

h2o.upload_mojo()

Imports a MOJO from a local filesystem, creating a Generic model with it.

h2o.var() var()

Variance of a column or covariance of columns.

h2o.varimp()

Retrieve the variable importance.

h2o.varimp_heatmap()

Variable Importance Heatmap across multiple models

h2o.varimp_plot()

Plot Variable Importances

h2o.varsplits()

Retrieve per-variable split information for a given Isolation Forest model. Output will include: - count - The number of times a variable was used to make a split. - aggregated_split_ratios - The split ratio is defined as "abs(#left_observations - #right_observations) / #before_split". Even splits (#left_observations approx the same as #right_observations) contribute less to the total aggregated split ratio value for the given feature; highly imbalanced splits (eg. #left_observations >> #right_observations) contribute more. - aggregated_split_depths - The sum of all depths of a variable used to make a split. (If a variable is used on level N of a tree, then it contributes with N to the total aggregate.)

h2o.week() week()

Convert Milliseconds to Week of Week Year in H2O Datasets

h2o.weights()

Retrieve the respective weight matrix

h2o.which_max() which.max.H2OFrame() which.min.H2OFrame()

Which indice contains the max value?

h2o.which_min()

Which index contains the min value?

h2o.which()

Which indices are TRUE?

h2o.withinss()

Get the Within SS

h2o.word2vec()

Trains a word2vec model on a String column of an H2O data frame

h2o.xgboost.available()

Determines whether an XGBoost model can be built

h2o.xgboost()

Build an eXtreme Gradient Boosting model

h2o.year() year()

Convert Milliseconds to Years in H2O Datasets

h2o.rulefit()

Build a RuleFit Model

H2OAutoML-class

The H2OAutoML class

H2OClusteringModel-class

The H2OClusteringModel object.

show(<H2OConnection>)

The H2OConnection class.

H2OConnectionMutableState

The H2OConnectionMutableState class

show(<H2OCoxPHModel>) coef(<H2OCoxPHModel>) extractAIC(<H2OCoxPHModel>) logLik(<H2OCoxPHModel>) survfit.H2OCoxPHModel() vcov(<H2OCoxPHModel>)

The H2OCoxPHModel object.

show(<H2OCoxPHModelSummary>) coef(<H2OCoxPHModelSummary>)

The H2OCoxPHModelSummary object.

H2OFrame-class

The H2OFrame class

`[`(<H2OFrame>) `$`(<H2OFrame>) `[[`(<H2OFrame>) `$`(<H2OFrame>) `[[`(<H2OFrame>) `[<-`(<H2OFrame>) `$<-`(<H2OFrame>) `[[<-`(<H2OFrame>)

Extract or Replace Parts of an H2OFrame Object

H2OInfogram-class

H2OInfogram class

show(<H2OGrid>)

H2O Grid

H2OLeafNode-class

The H2OLeafNode class.

show(<H2OModel>)

The H2OModel object.

H2OModelFuture-class

H2O Future Model

show(<H2OModelMetrics>) show(<H2OBinomialMetrics>) show(<H2OBinomialUpliftMetrics>) show(<H2OMultinomialMetrics>) show(<H2OOrdinalMetrics>) show(<H2ORegressionMetrics>) show(<H2OClusteringMetrics>) show(<H2OAutoEncoderMetrics>) show(<H2ODimReductionMetrics>) show(<H2OAnomalyDetectionMetrics>)

The H2OModelMetrics Object.

show(<H2ONode>)

The H2ONode class.

H2OSplitNode-class

The H2OSplitNode class.

show(<H2OTree>)

The H2OTree class.

housevotes

United States Congressional Voting Records 1984

iris

Edgar Anderson's Iris Data

is.character()

Check if character

is.factor()

Check if factor

is.h2o()

Is H2O Frame object

is.numeric()

Check if numeric

length(<H2OTree>)

Overrides the behavior of length() function on H2OTree class. Returns number of nodes in an H2OTree

`||`()

Logical or for H2OFrames

getParms() getCenters() getCentersStd() getWithinSS() getTotWithinSS() getBetweenSS() getTotSS() getIterations() getClusterSizes()

Accessor Methods for H2OModel Object

names(<H2OFrame>)

Column names of an H2OFrame

Ops(<H2OFrame>) Math(<H2OFrame>) Math(<H2OFrame>) Math(<H2OFrame>) Summary(<H2OFrame>) `!`(<H2OFrame>) is.na(<H2OFrame>) t(<H2OFrame>) log() log10() log2() log1p() trunc() `%*%` nrow.H2OFrame() ncol.H2OFrame() length(<H2OFrame>) h2o.length() `names<-`(<H2OFrame>) `colnames<-`()

S3 Group Generic Functions for H2O

plot(<H2OInfogram>)

Plot an H2O Infogram

plot(<H2OModel>)

Plot an H2O Model

plot(<H2OTabulate>)

Plot an H2O Tabulate Heatmap

predict(<H2OAutoML>) h2o.predict(<H2OAutoML>)

Predict on an AutoML object

predict(<H2OModel>) h2o.predict(<H2OModel>)

Predict on an H2O Model

predict_contributions.H2OModel() h2o.predict_contributions()

Predict feature contributions - SHAP values on an H2O Model (only DRF, GBM, XGBoost models and equivalent imported MOJOs).

row_to_tree_assignment.H2OModel() h2o.row_to_tree_assignment()

Output row to tree assignment for the model and provided training data.

predict_leaf_node_assignment.H2OModel() h2o.predict_leaf_node_assignment()

Predict the Leaf Node Assignment on an H2O Model

print(<H2OFrame>)

Print An H2OFrame

print(<H2OTable>)

Print method for H2OTable objects

prostate

Prostate Cancer Study

range(<H2OFrame>)

Range of an H2O Column

scale(<H2OFrame>)

Scaling and Centering of an H2OFrame

staged_predict_proba.H2OModel() h2o.staged_predict_proba()

Predict class probabilities at each stage of an H2O Model

str(<H2OFrame>)

Display the structure of an H2OFrame object

summary(<H2OCoxPHModel>)

Summary method for H2OCoxPHModel objects

summary(<H2OGrid>)

Format grid object in user-friendly way

summary(<H2OModel>)

Print the Model Summary

use.package()

Use optional package

walking

Muscular Actuations for Walking Subject