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.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
`h2o-package`	H2O R Interface
`h2o.abs()`	Compute the absolute value of x
`h2o.acos()`	Compute the arc cosine of x
`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.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.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.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.decryptionSetup()`	Setup a Decryption Tool
`h2o.deepfeatures()`	Feature Generation via H2O Deep Learning or DeepWater Model
`h2o.deeplearning()`	Build a Deep Neural Network model using CPUs
`h2o.deepwater.available()`	Determines whether Deep Water is available
`h2o.deepwater()`	Build a Deep Learning model using multiple native GPU backends
`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_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.entropy()`	Shannon entropy
`h2o.exp()`	Compute the exponential function of x
`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.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()`	Access H2O Gains/Lift Tables
`h2o.gbm()`	Build gradient boosted classification or regression trees
`h2o.getAutoML()`	Get an R object that is a subclass of H2OAutoML
`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.getFutureModel()`	Get future model
`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 Cloud Returns a string
`h2o.getTypes()`	Get the types-per-column
`h2o.getVersion()`	Get h2o version
`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.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.ifelse()` `ifelse()`	H2O Apply Conditional Statement
`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.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.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.kurtosis()` `kurtosis.H2OFrame()`	Kurtosis of a column
`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.listTimezones()`	List all of the Time Zones Acceptable by the H2O Cloud.
`h2o.loadModel()`	Load H2O Model from HDFS or Local Disk
`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.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.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.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.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.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.parseRaw()`	H2O Data Parsing
`h2o.parseSetup()`	Get a parse setup back for the staged data.
`h2o.partialPlot()`	Partial Dependence Plots
`h2o.performance()`	Model Performance Metrics in H2O
`h2o.pivot()`	Pivot a frame
`h2o.prcomp()`	Principal component analysis of an H2O data frame
`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.pr_auc()`	Retrieve the pr_auc
`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.residual_deviance()`	Retrieve the residual deviance
`h2o.residual_dof()`	Retrieve the residual degrees of freedom
`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.saveModel()`	Save an H2O Model Object to Disk
`h2o.saveModelDetails()`	Save an H2O Model Details
`h2o.saveMojo()`	Save an H2O Model Object as Mojo to Disk
`h2o.scale()` `scale(<H2OFrame>)`	Scaling and Centering of an H2OFrame
`h2o.scoreHistory()`	Retrieve Model Score History
`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 Cloud
`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.toFrame()`	Convert a word2vec model into an H2OFrame
`h2o.tokenize()`	Tokenize String
`h2o.tolower()`	Convert strings to lowercase
`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.transform()`	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.var()` `var()`	Variance of a column or covariance of columns.
`h2o.varimp_plot()`	Plot Variable Importances
`h2o.varimp()`	Retrieve the variable importance.
`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
`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
`show(<H2OGrid>)`	H2O Grid
`H2OLeafNode-class`	The H2OLeafNode class.
`show(<H2OModel>)`	The H2OModel object.
`H2OModelFuture-class`	H2O Future Model
`show(<H2OModelMetrics>)` `show(<H2OBinomialMetrics>)` `show(<H2OMultinomialMetrics>)` `show(<H2OOrdinalMetrics>)` `show(<H2ORegressionMetrics>)` `show(<H2OClusteringMetrics>)` `show(<H2OAutoEncoderMetrics>)` `show(<H2ODimReductionMetrics>)`	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
`\|\|`()	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(<H2OModel>)`	Plot an H2O Model
`plot(<H2OTabulate>)`	Plot an H2O Tabulate Heatmap
`predict(<H2OAutoML>)`	Predict on an AutoML object
`predict(<H2OModel>)` `h2o.predict()`	Predict on an H2O Model
`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
`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

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