Function reference • h2o

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.all_constraints_passed()`	Return the TRUE if all constraints are satisfied for a constraint GLM model and FALSE otherwise. If you want to check which constraint failed, use h2o.get_constraints_info method.
`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_names()`	Return the GLM coefficient names without building the actual GLM model by setting max_iterations=0.
`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_random()`	Extracts the random effects coefficients of an HGLM model.
`h2o.coefs_random_names()`	Extracts the coefficient names of random effect coefficients.
`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_constraints_info()`	Return the GLM linear constraints descriptions, constraints values, constraints bounds and whether the constraints satisfied the bounds (true) or not (false)
`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.hglm()`	Fits a HGLM model with both the residual noise and random effect being modeled by Gaussian distribution. The fixed effect coefficients are specified in parameter x, the random effect coefficients are specified in parameter random_columns. The column specified in group_column will contain the level 2 index value and must be an enum column.
`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.icc()`	Extracts the ICC of the HGLM model.
`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.level_2_names()`	Extracts the group_column levels of an HGLM model. The group_column is usually referred to as level 2 predictor.
`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.matrix_T()`	Extracts T matrix which is the covariance of random effect coefficients.
`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_residual_fixed()`	Extracts the mean residual error taking into account only the fixed effect coefficients.
`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.residual_variance()`	Extracts the variance of residuals of the HGLM model.
`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.scoring_history()`	Extracts scoring history of training dataframe during training
`h2o.scoring_history_valid()`	Extracts scoring history of validation dataframe during training
`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

Reference

Reference