-
h2o.downloadCSV : Download a H2O dataset to a CSV file on a local disk.
-
h2o.exportFile: ExpExport H2O Data Frame to a file
-
h2o.importFile: Import a file from the local path and parse it.
-
h2o.parseRaw: Parse a raw data file
-
h2o.uploadFile: Upload a file from the local drive and parse it.
- as.h2o: Convert an R object to an H2O object.
- as.data.frame: Check if an object is a data frame, or coerce it if possible.
- h2o.createFrame: Create an H2O data frame, with optional
-
h2o.runif: Produce a vector of random uniform numbers.
-
h2o.interaction: Create interaction terms between categorical features of an H2O Frame.
- h2o.splitFrame: Split an existing H2O dataset according to user-specified ratios.
-
h2o.impute: Impute a column of data using the mean, median, or mode.
-
h2o.insertMissingValues: Replaces a user specified fraction of entries in a H2O dataset with missing values.
- head, tail: Return the First or Last Part of an Object.
-
abs: Compute the absolute value of x.
-
sign: Return a vector with the signs of the corresponding elements of x (the sign of a real number is 1, 0, or -1 if the number is positive, zero, or negative, respectively).
-
sqrt: Computes the principal square root of x, √x.
-
ceiling: Take a single numeric argument x and return a numeric vector containing the smallest integers not less than the corresponding elements of x.
-
floor: Take a single numeric argument x and return a numeric vector containing the largest integers not greater than the corresponding elements of x.
-
trunc: Take a single numeric argument x and return a numeric vector containing the integers formed by truncating the values in x toward 0.
-
log: Compute logarithms (by default, natural logarithms).
-
exp: Compute the exponential function.
-
cummax: Display a vector of the cumulative maxima of the elements of the argument.
-
cummin: Display a vector of the cumulative minima of the elements of the argument.
-
cumprod: Display a vector of the cumulative products of the elements of the argument.
-
log10: Compute common (i.e., base 10) logarithms
-
log2: Compute binary (i.e., base 2) logarithms.
-
log1p: Compute log(1+x) accurately also for |x|<< 1.
-
acos: Compute the trigonometric arc-cosine.
-
acosh: Compute the hyperbolic arc-cosine.
-
asin: Compute the trigonometric arc-sine.
-
asinh: Compute the hyperbolic arc-sine.
-
atan: Compute the trigonometric arc-tangent.
-
atanh: Compute the hyperbolic arc-tangent.
-
expm1: Compute exp(x) - 1 accurately also for |x|<< 1.
-
cos: Compute the trigonometric cosine.
-
sinh: Compute the hyperbolic sine.
-
sinpi: Compute the trigonometric two-argument arc-sine.
-
tan: Compute the trigonometric tangent.
-
tanh: Compute the hyperbolic tangent.
-
tanpi: Compute the trigonometric two-argument arc-tangent.
-
gamma: Display the gamma function γx
-
lgamma: Display the natural logarithm of the absolute value of the gamma function.
-
digamma: Display the first derivative of the logarithm of the gamma function.
-
trigamma: Display the second derivative of the logarithm of the gamma function.
-
round: Round the values to the specified number of decimal places. The default is 0.
-
signif: Round the values to the specified number of significant digits.
- max: Display the maximum of all the input arguments.
- min: Display the minimum of all the input arguments.
- range: Display a vector containing the minimum and maximum of all the given arguments.
- sum: Calculate the sum of all the values present in its arguments.
- prod: Display the product of all values present in its arguments.
- any: Given a set of logical vectors, determine if at least one of the values is true.
- all: Given a set of logical vectors, determine if all of the values are true.
- mean: Generic function for the (trimmed) arithmetic mean.
- sd: Calculate the standard deviation of a column of continuous real valued data.
- var: Compute the variance of x.
- summary: Produce result summaries of the results of various model fitting functions.
- quantile: Obtain and display quantiles for H2O parsed data. Row / Column Aggregation
- apply: Apply a function over an H2O parsed data object (an array). Group By Aggregation
- h2o.group by: Apply an aggregate function to each group of an H2O dataset.
- h2o.table: Use the cross-classifying factors to build a table of counts at each combination of factor levels.
- is.na: Display missing elements.
- h2o.which: Display the row numbers for which the condition is true.
- h2o.ifelse: Apply conditional statements to numeric vectors in H2O parsed data objects.
- h2o.cut: Convert H2O Numeric Data to Factor.
- h2o.strsplit: Splits the given factor column on the input split.
- h2o.tolower: Change the elements of a character vector to lower case.
- h2o.toupper: Change the elements of a character vector to lower case.
- h2o.trim: Remove leading and trailing white space.
- h2o.gsub: Match a pattern & replace all instances of the matched pattern with the replacement string globally.
- h2o.sub: Match a pattern & replace the first instance of the matched pattern with the replacement string.
*h2o.levels: Display a list of the unique values found in a column of categorical data.
- h2o.month: Convert the entries of a H2OParsedData object from milliseconds to months (on a 0 to 11 scale).
- h2o.year: Convert the entries of a H2OParsedData object from milliseconds to years, indexed starting from 1900.
- %∗%: Multiply two matrices, if they are conformable.
- t: Given a matrix or data.frame x, t returns the transpose of x.
-
h2o.deeplearning: Perform Deep Learning neural networks on an
-
H2OParsedData object.
-
h2o.gbm: Build gradient boosted classification trees and gradient boosted regression trees on a parsed dataset.
-
h2o.glm: Fit a generalized linear model, specified by a response variable, a set of predictors, and a description of the error distribution.
-
h2o.naiveBayes: Build gradient boosted classification trees and gradient boosted regression trees on a parsed dataset.
-
h2o.prcomp: Perform principal components analysis on the given dataset.
-
h2o.randomForest: Perform random forest classification on a dataset.
-
h2o.xgboost: Build an extreme gradient boosted model.
- h2o.anomaly: Detect anomalies in a H2O dataset using a H2O deep learning model with auto-encoding.
- h2o.deepfeatures: Extract the non-linear features from a H2O dataset using a H2O deep learning model.
- h2o.kmeans: Perform k-means clustering on a dataset.
- h2o.grid: Efficient method to build multiple models with different
- h2o.predict: Obtain predictions from various fitted H2O model objects.
- ho2.model metrics: Given predicted values (target for regression, class-1 probabilities, or binomial or per-class probabilities for multinomial), compute a model metrics object.
- h2o.accuracy: Get the between cluster sum of squares.
- h2o.auc: Retrieve the AUC (area under ROC curve).
- h2o.confusionMatrix: Display prediction errors for classification data from a column of predicted responses and a column of actual (reference) responses in H2O.
- h2o.hit ratio table: Retrieve the Hit Ratios. If train, valid, and xval parameters are FALSE (default), then the training Hit Ratios value is returned. If more than one parameter is seto TRUE, then a named list of Hit Ratio tables are returned, where the names are train, valid, or xval.
- h2o.performance: Evaluate the predictive performance of a model via various measures.
- h2o.mse: Display the mean squared error calculated from a column of predicted responses and a column of actual (reference) responses in H2O.
- h2o.betweenss: Get the between cluster sum of squares.
- h2o.centers: Retrieve the Model Centers.
- h2o.assign: Assign H2O hex.keys to objects in their R environment.
- h2o.getFrame: Get a reference to an existing H2O dataset.
- h2o.getModel: Get a reference to an existing H2O model.
- h2o.ls: Display a list of object keys in the running instance of H2O.
- h2o.rm: Remove H2O objects from the server where the instance of H2O is running, but does not remove it from the R environment.
h2o.saveModel: Save an H2OModel object to disk to be loaded back into
- h2o.init (nthreads = -1): Connect to a running H2O instance using all CPUs on the host and check the local H2O R package is the correct version.
- h2o.shutdown: Shut down the specified H2O instance. All data on the server will be lost!
- h2o.rebalance: Rebalance (repartition) an existing H2O dataset into given number of chunks (per Vec), for load-balancing across multiple threads or nodes.
- h2o.clusterInfo: Display the name, version, uptime, total nodes, total memory, total cores and health of a cluster running H2O.
- h2o.clusterStatus: Retrieve information on the status of the cluster running H2O.
- h2o.clearLog: Clear all H2O R command and error response logs from the local disk.
- h2o.downloadAllLogs: Download all H2O log files to the local disk.
- h2o.logAndEcho: Write a message to the H2O Java log file and echo it back.
- h2o.openLog: Open existing logs of H2O R POST commands and error responses on the local disk.
- h2o.getLogPath: Get the file path for the H2O R command and error response logs.
- h2o.startLogging: Begin logging H2O R POST commands and error responses.
- h2o.stopLogging: Stop logging H2O R POST commands and error responses.
- h2o.gsub: String global substitution (all occurrences).
- h2o.strsplit: String Split.
- h2o.sub: String substitution (first occurrence).
- h2o.tolower: Convert characters to lower case.
- h2o.toupper: Convert characters to upper case.
- h2o.trim: Trim spaces.