Package index • h2o

All functions

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>) `$`(<H2OFrame>) `[[`(<H2OFrame>) `[<-`(<H2OFrame>) `$<-`(<H2OFrame>) `[[<-`(<H2OFrame>): Extract or Replace Parts of an H2OFrame Object
H2OFrame-class: The H2OFrame class
Ops(<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
show(<H2OGrid>): H2O Grid
H2OInfogram-class: H2OInfogram class
H2OInfogram(): wrapper function for instantiating H2OInfogram
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.
show(<H2OSegmentModels>): H2O Segment Models
H2OSegmentModelsFuture-class: H2O Future Segment Models
H2OSplitNode-class H2OSplitNode: The H2OSplitNode class.
show(<H2OTree>): The H2OTree class.
Keyed-class: Virtual Keyed class
`||`(): Logical or for H2OFrames
getParms() getCenters() getCentersStd() getWithinSS() getTotWithinSS() getBetweenSS() getTotSS() getIterations() getClusterSizes(): Accessor Methods for H2OModel Object
aaa: Starting H2O For examples
`&&`(): Logical and for H2OFrames
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
case_insensitive_match_arg(): Works like match.arg but ignores case
colnames(): Returns the column names of an H2OFrame
dim(<H2OFrame>): Returns the Dimensions of an H2OFrame
dimnames(<H2OFrame>): Column names of an H2OFrame
.addParm(): TODO: No objects in this file are being used. Either remove file or use objects.
.check_for_ggplot2(): Stop with a user friendly message if a user is missing the ggplot2 package or has an old version of it.
.collapse(): Helper Collapse Function
.consolidate_varimps(): Consolidate variable importances
.create_leaderboard(): Create a leaderboard like data frame for models
.customized_call(): A helper function that makes it easier to override/add params in a function call.
.find_appropriate_column_name(): Tries to match a fuzzy_col_name with a column name that exists in cols.
.get_algorithm(): Get the algoritm used by the model_or_model_id
.get_domain_mapping(): Get a mapping between columns and their domains
.get_feature_count(): Get feature count sorted by the count descending.
.get_first_of_family(): Get first of family models
.h2o.__ALL_CAPABILITIES: Capabilities endpoints
.h2o.__CREATE_FRAME: H2OFrame Manipulation
.h2o.__DECRYPTION_SETUP: Decryption Endpoints
.h2o.__DKV: Removal Endpoints
.h2o.__EXPORT_FILES(): Export Files Endpoint Generator
.h2o.__FRAMES: Inspect/Summary Endpoints
.h2o.__IMPORT: Import/Export Endpoints
.h2o.__JOBS: Administrative Endpoints
.h2o.__LOGANDECHO: Log and Echo Endpoint
.h2o.__MODELS: Model Endpoint
.h2o.__MODEL_BUILDERS(): Model Builder Endpoint Generator
.h2o.__MODEL_METRICS(): Model Metrics Endpoint
.h2o.__PARSE_SETUP: Parse Endpoints
.h2o.__RAPIDS: Rapids Endpoint
.h2o.__REST_API_VERSION: H2O Package Constants
.h2o.__SEGMENT_MODELS_BUILDERS(): Segment Models Builder Endpoint Generator
.h2o.__W2V_SYNONYMS: Word2Vec Endpoints
.h2o.__checkConnectionHealth(): Check H2O Server Health
.h2o.doGET(): Just like doRawGET but fills in the default h2oRestApiVersion if none is provided
.h2o.doPOST(): Just like doRawPOST but fills in the default h2oRestApiVersion if none is provided
.h2o.doRawGET(): Perform a low-level HTTP GET operation on an H2O instance
.h2o.doRawPOST(): Perform a low-level HTTP POST operation on an H2O instance
.h2o.doSafeGET(): Perform a safe (i.e. error-checked) HTTP GET request to an H2O cluster.
.h2o.doSafePOST(): Perform a safe (i.e. error-checked) HTTP POST request to an H2O cluster.
.h2o.is_progress(): Check if Progress Bar is Enabled
.h2o.locate(): Locate a file given the pattern <bucket>/<path/to/file> e.g. h2o:::.h2o.locate("smalldata/iris/iris22.csv") returns the absolute path to iris22.csv
.h2o.perfect_auc(): Internal function that calculates a precise AUC from given probabilities and actual responses.
.h2o.primitives: Map of operations known to H2O
.has_model_coefficients(): Has the model coefficients?
.has_varimp(): Has the model variable importance?
.interpretable(): Is the model considered to be interpretable, i.e., simple enough.
.is_h2o_model(): Is the model an H2O model?
.is_h2o_tree_model(): Is the model a Tree-based H2O Model?
.is_plotting_to_rnotebook(): Check if we are plotting in to r notebook.
.leaderboard_for_row(): Enhance leaderboard with per-model predictions.
.min_max(): Min-max normalization.
.model_ids(): Get Model Ids
.pkg.env: The H2O Package Environment
.plot_varimp(): Plot variable importances with ggplot2
.process_models_or_automl(): Do basic validation and transform object to a "standardized" list containing models, and their properties such as x, y, whether it is a (multinomial) clasification or not etc.
.shorten_model_ids(): Shortens model ids if possible (iff there will be same amount of unique model_ids as before)
.skip_if_not_developer(): H2O <-> R Communication and Utility Methods
.uniformize(): Convert to quantiles when provided with numeric vector. When col is a factor vector assign uniformly value between 0 and 1 to each level.
.varimp(): Get variable importance in a standardized way.
.verify_dataxy(): Used to verify data, x, y and turn into the appropriate things
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.
generate_col_ind(): CHeck to see if the column names/indices entered is valid for the dataframe given. This is an internal function
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: H2O R Interface
h2o.HGLMMetrics(): Retrieve HGLM ModelMetrics
h2o.abs(): Compute the absolute value of x
h2o.acos(): Compute the arc cosine of x
h2o.adaBoost(): Build an AdaBoost model
h2o.aecu(): Retrieve the default AECU (Average Excess Cumulative Uplift = area between AUUC and random AUUC)
h2o.aecu_table(): Retrieve the all types of AECU (average excess cumulative uplift) value in a table
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.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.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.atc(): Retrieve Average Treatment Effect on the Control
h2o.ate(): Retrieve Average Treatment Effect
h2o.att(): Retrieve Average Treatment Effect on the Treated
h2o.auc(): Retrieve the AUC
h2o.aucpr() h2o.pr_auc(): Retrieve the AUCPR (Area Under Precision Recall Curve)
h2o.automl(): Automatic Machine Learning
h2o.auuc(): Retrieve AUUC
h2o.auuc_normalized(): Retrieve normalized AUUC
h2o.auuc_table(): Retrieve the all types of AUUC in a table
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.calculate_fairness_metrics(): Calculate intersectional fairness metrics.
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.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.cluster_sizes(): Retrieve the cluster sizes
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.disparate_analysis(): Create a frame containing aggregations of intersectional fairness across the models.
h2o.distance(): Compute a pairwise distance measure between all rows of two numeric H2OFrames.
h2o.downloadAllLogs(): Download H2O Log Files to Disk
h2o.downloadCSV(): Download H2O Data to Disk
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.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.extendedIsolationForest(): Trains an Extended Isolation Forest model
h2o.fair_pd_plot(): Partial dependence plot per protected group.
h2o.fair_pr_plot(): Plot PR curve per protected group.
h2o.fair_roc_plot(): Plot ROC curve per protected group.
h2o.fair_shap_plot(): SHAP summary plot for one feature with protected groups on y-axis.
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.findSynonyms(): Find synonyms using a word2vec model.
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.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(<H2OModel>): Plot Gains/Lift curves
h2o.gains_lift_plot(<H2OModelMetrics>): Plot Gains/Lift curves
h2o.gains_lift_plot(): Plot Gains/Lift curves
h2o.gam(): Fit a General Additive Model
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.getAlphaBest(): Extract best alpha value found from glm model.
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.getLambdaBest(): Extract best lambda value found from glm model.
h2o.getLambdaMax(): Extract the maximum lambda value used during lambda search from glm model.
h2o.getLambdaMin(): Extract the minimum lambda value calculated during lambda search from glm model. Note that due to early stop, this minimum lambda value may not be used in the actual lambda search.
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.get_automl() h2o.getAutoML(): Get an R object that is a subclass of H2OAutoML
h2o.get_best_model(): Get best model of a given family/algorithm for a given criterion from an AutoML object.
h2o.get_best_model_predictors(): Extracts the subset of predictor names that yield the best R2 value for each predictor subset size.
h2o.get_best_r2_values(): Extracts the best R2 values for all 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_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_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_segment_models(): Retrieves an instance of H2OSegmentModels for a given id.
h2o.get_variable_inflation_factors(): Return the variable inflation factors associated with numerical predictors for GLM models.
h2o.giniCoef(): Retrieve the GINI Coefficcient
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(): H2O Grid Support
h2o.group_by(): Group and Apply by Column
h2o.gsub(): String Global Substitute
h2o.h(): Calculates Friedman and Popescu's H statistics, in order to test for the presence of an interaction between specified variables in h2o gbm and xgb models. H varies from 0 to 1. It will have a value of 0 if the model exhibits no interaction between specified variables and a correspondingly larger value for a stronger interaction effect between them. NaN is returned if a computation is spoiled by weak main effects and rounding errors.
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.importFile() h2o.importFolder() h2o.importHDFS() h2o.uploadFile(): Import Files into H2O
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.impute(): Basic Imputation of H2O Vectors
h2o.infogram(): H2O Infogram
h2o.infogram_train_subset_models(): Train models over subsets selected using infogram
h2o.init(): Initialize and Connect to H2O
h2o.insertMissingValues(): Insert Missing Values into an H2OFrame
h2o.inspect_model_fairness(): Produce plots and dataframes related to a single model fairness.
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.isotonicregression(): Build an Isotonic Regression model
h2o.keyof(): Method on Keyed objects allowing to obtain their key.
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.listTimezones(): List all of the Time Zones Acceptable by the H2O cluster.
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.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.log10(): Compute the log10 of x
h2o.log1p(): Compute the log1p of x
h2o.log2(): Compute the log2 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.makeGLMModel(): Set betas of an existing H2O GLM Model
h2o.make_leaderboard(): Create a leaderboard from a list of models, grids and/or automls.
h2o.make_metrics(): Create Model Metrics from predicted and actual values in H2O
h2o.match() match.H2OFrame() `%in%`: Value Matching in H2O
h2o.max(): Returns the maxima of the input values.
h2o.mean() mean(<H2OFrame>): Compute the frame's mean by-column (or by-row).
h2o.mean_per_class_error(): Retrieve the mean per class error
h2o.mean_residual_deviance(): Retrieve the Mean Residual Deviance value
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.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.model_correlation(): Model Prediction Correlation
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.multinomial_auc_table(): Retrieve the all AUC values in a table (One to Rest, One to One, macro and weighted average) for mutlinomial classification.
h2o.multinomial_aucpr_table(): Retrieve the all PR AUC values in a table (One to Rest, One to One, macro and weighted average) for mutlinomial classification.
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.predict_rules(): Evaluates validity of the given rules on the given data. Returns a frame with a column per each input rule id, representing a flag whether given rule is applied to the observation or not.
h2o.predicted_vs_actual_by_variable(): Calculates per-level mean of predicted value vs actual value for a given variable.
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.psvm(): Trains a Support Vector Machine model on an H2O dataset
h2o.qini(): Retrieve the default Qini value
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.rapids(): Execute a Rapids expression.
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.relevel_by_frequency(): Reorders levels of factor columns by the frequencies for the individual levels.
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.resume(): Triggers auto-recovery resume - this will look into configured recovery dir and resume and tasks that were interrupted by unexpected cluster stopping.
h2o.resumeGrid(): Resume previously stopped grid training.
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.rule_importance(): This function returns the table with estimated coefficients and language representations (in case it is a rule) for each of the significant baselearners.
h2o.rulefit(): Build a RuleFit Model
h2o.runif(): Produce a Vector of Random Uniform Numbers
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.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.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.screeplot(): Scree Plot
h2o.sd() sd(): Standard Deviation of a column of data.
h2o.sdev(): Retrieve the standard deviations of principal components
h2o.setLevels(): Set Levels of H2O Factor Column
h2o.setTimezone(): Set the Time Zone on the H2O cluster
h2o.set_s3_credentials(): Creates a new Amazon S3 client internally with specified credentials.
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.thresholds_and_metric_scores(): Retrieve the thresholds and metric scores table
h2o.toFrame(): Convert a word2vec model into an H2OFrame
h2o.tokenize(): Tokenize String
h2o.tolower(): Convert strings to lowercase
h2o.topBottomN(): H2O topBottomN
h2o.topN(): H2O topN
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(): H2O Segmented-Data Bulk Model Training
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.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.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(<H2OAutoML>): Retrieve the variable importance.
h2o.varimp(<H2OFrame>): Retrieve the variable importance.
h2o.varimp(<H2OModel>): Retrieve the variable importance.
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(): Which indices are TRUE?
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.withinss(): Get the Within SS
h2o.word2vec(): Trains a word2vec model on a String column of an H2O data frame
h2o.xgboost(): Build an eXtreme Gradient Boosting model
h2o.xgboost.available(): Determines whether an XGBoost model can be built
h2o.year() year(): Convert Milliseconds to Years in H2O Datasets
housevotes: United States Congressional Voting Records 1984
initialize(<H2OInfogram>): Method on H2OInfogram object which in this case is to instantiate and initialize it
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
model_cache-class .model_cache: Needed to be able to memoise the models
names(<H2OFrame>): Column names of an H2OFrame
plot(<H2OParetoFront>): Plot Pareto front
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).
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
row_to_tree_assignment.H2OModel() h2o.row_to_tree_assignment(): Output row to tree assignment for the model and provided training data.
scale(<H2OFrame>): Scaling and Centering of an H2OFrame
show(<H2OAutoML>): Format AutoML object in user-friendly way
show(<H2OParetoFront>): Show H2OParetoFront
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(<H2OAutoML>): Format AutoML object in user-friendly way
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
zzz: Shutdown H2O cluster after examples run