h2o v3.30.0.1
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R Interface for the 'H2O' Scalable Machine Learning Platform
R interface for 'H2O', the scalable open source machine learning
platform that offers parallelized implementations of many supervised and
unsupervised machine learning algorithms such as Generalized Linear
Models, Gradient Boosting Machines (including XGBoost), Random Forests,
Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes, Cox
Proportional Hazards, K-Means, PCA, Word2Vec, as well as a fully automatic
machine learning algorithm (AutoML).
Functions in h2o
| Name | Description | |
| H2ONode-class | The H2ONode class. | |
| .h2o.__MODELS | Model Endpoint | |
| H2OLeafNode-class | The H2OLeafNode class. | |
| .collapse | Helper Collapse Function | |
| H2OSegmentModelsFuture-class | H2O Future Segment Models | |
| apply | Apply on H2O Datasets | |
| as.character.H2OFrame | Convert an H2OFrame to a String | |
| H2OCoxPHModelSummary-class | The H2OCoxPHModelSummary object. | |
| H2OSplitNode-class | The H2OSplitNode class. | |
| H2OSegmentModels-class | H2O Segment Models | |
| .h2o.__ALL_CAPABILITIES | Capabilities endpoints | |
| H2OTree-class | The H2OTree class. | |
| colnames | Returns the column names of an H2OFrame | |
| 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 | |
| Keyed-class | Virtual Keyed class | |
| dim.H2OFrame | Returns the Dimensions of an H2OFrame | |
| .h2o.__DKV | Removal Endpoints | |
| H2OModelFuture-class | H2O Future Model | |
| H2OModelMetrics-class | The H2OModelMetrics Object. | |
| .h2o.__FRAMES | Inspect/Summary Endpoints | |
| H2OModel-class | The H2OModel object. | |
| .h2o.__MODEL_BUILDERS | Model Builder Endpoint Generator | |
| ModelAccessors | Accessor Methods for H2OModel Object | |
| Logical-or | Logical or for H2OFrames | |
| as.matrix.H2OFrame | Convert an H2OFrame to a matrix | |
| aaa | Starting H2O For examples | |
| .h2o.__REST_API_VERSION | H2O Package Constants | |
| && | Logical and for H2OFrames | |
| h2o.aucpr | Retrieve the AUCPR (Area Under Precision Recall Curve) | |
| dimnames.H2OFrame | Column names of an H2OFrame | |
| h2o.automl | Automatic Machine Learning | |
| .h2o.doRawPOST | Perform a low-level HTTP POST operation on an H2O instance | |
| .h2o.__CREATE_FRAME | H2OFrame Manipulation | |
| .addParm | TODO: No objects in this file are being used. Either remove file or use objects. | |
| .verify_dataxy | Used to verify data, x, y and turn into the appropriate things | |
| .h2o.__PARSE_SETUP | Parse Endpoints | |
| as.numeric | Convert H2O Data to Numeric | |
| feature_frequencies.H2OModel | Retrieve the number of occurrences of each feature for given observations Available for GBM, Random Forest and Isolation Forest models. | |
| h2o.aggregated_frame | Retrieve an aggregated frame from an Aggregator model | |
| h2o.aic | Retrieve the Akaike information criterion (AIC) value | |
| as.h2o | Create H2OFrame | |
| as.factor | Convert H2O Data to Factors | |
| as.vector.H2OFrame | Convert an H2OFrame to a vector | |
| australia | Australia Coastal Data | |
| .h2o.__DECRYPTION_SETUP | Decryption Endpoints | |
| .h2o.__EXPORT_FILES | Export Files Endpoint Generator | |
| h2o.assign | Rename an H2O object. | |
| .h2o.doPOST | Just like doRawPOST but fills in the default h2oRestApiVersion if none is provided | |
| h2o.betweenss | Get the between cluster sum of squares | |
| h2o.biases | Return the respective bias vector | |
| h2o.auc | Retrieve the AUC | |
| h2o.aggregator | Build an Aggregated Frame | |
| .h2o.__JOBS | Administrative Endpoints | |
| .h2o.__checkConnectionHealth | Check H2O Server Health | |
| h2o.centers | Retrieve the Model Centers | |
| .h2o.__IMPORT | Import/Export Endpoints | |
| .h2o.__SEGMENT_MODELS_BUILDERS | Segment Models Builder Endpoint Generator | |
| .h2o.doGET | Just like doRawGET but fills in the default h2oRestApiVersion if none is provided | |
| .h2o.is_progress | Check if Progress Bar is Enabled | |
| .h2o.doSafePOST | Perform a safe (i.e. error-checked) HTTP POST request to an H2O cluster. | |
| .h2o.__LOGANDECHO | Log and Echo Endpoint | |
| .h2o.doRawGET | Perform a low-level HTTP GET operation on an H2O instance | |
| generate_col_ind | CHeck to see if the column names/indices entered is valid for the dataframe given. This is an internal function | |
| .h2o.doSafeGET | Perform a safe (i.e. error-checked) HTTP GET request to an H2O cluster. | |
| h2o.gainsLift | Access H2O Gains/Lift Tables | |
| h2o.clusterInfo | Print H2O cluster info | |
| h2o.createFrame | Data H2OFrame Creation in H2O | |
| h2o.clearLog | Delete All H2O R Logs | |
| .pkg.env | The H2O Package Environment | |
| get_seed.H2OModel | 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.cumsum | Return the cumulative sum over a column or across a row | |
| h2o.cross_validation_fold_assignment | Retrieve the cross-validation fold assignment | |
| h2o.cut | Cut H2O Numeric Data to Factor | |
| .skip_if_not_developer | H2O <-> R Communication and Utility Methods | |
| .h2o.__W2V_SYNONYMS | Word2Vec Endpoints | |
| h2o.download_pojo | Download the Scoring POJO (Plain Old Java Object) of an H2O Model | |
| h2o.dim | Returns the number of rows and columns for an H2OFrame object. | |
| h2o.exportHDFS | Export a Model to HDFS | |
| h2o.difflag1 | Conduct a lag 1 transform on a numeric H2OFrame column | |
| h2o.fillna | fillNA | |
| h2o-package | H2O R Interface | |
| h2o.floor | Take a single numeric argument and return a numeric vector with the largest integers | |
| h2o.HGLMMetrics | Retrieve HGLM ModelMetrics | |
| h2o.flow | Open H2O Flow | |
| h2o.getGLMFullRegularizationPath | Extract full regularization path from a GLM model | |
| h2o.abs | Compute the absolute value of x | |
| h2o.anomaly | Anomaly Detection via H2O Deep Learning Model | |
| h2o.getFrame | Get an R Reference to an H2O Dataset, that will NOT be GC'd by default | |
| h2o.all | Given a set of logical vectors, are all of the values true? | |
| h2o.acos | Compute the arc cosine of x | |
| .h2o.__RAPIDS | Rapids Endpoint | |
| .h2o.primitives | Map of operations known to H2O | |
| h2o.centersSTD | Retrieve the Model Centers STD | |
| h2o.centroid_stats | Retrieve centroid statistics | |
| 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.any | Given a set of logical vectors, is at least one of the values true? | |
| h2o.colnames | Return column names of an H2OFrame | |
| h2o.as_date | Convert between character representations and objects of Date class | |
| .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.columns_by_type | Obtain a list of columns that are specified by `coltype` | |
| h2o.genericModel | Imports a model under given path, creating a Generic model with it. | |
| h2o.cross_validation_holdout_predictions | Retrieve the cross-validation holdout predictions | |
| h2o.bottomN | H2O bottomN | |
| h2o.ascharacter | Convert H2O Data to Characters | |
| h2o.is_client | Check Client Mode Connection | |
| h2o.get_leaderboard | Retrieve the leaderboard from the AutoML instance. | |
| h2o.cbind | Combine H2O Datasets by Columns | |
| h2o.import_sql_select | Import SQL table that is result of SELECT SQL query into H2O | |
| h2o.get_ntrees_actual | Retrieve actual number of trees for tree algorithms | |
| h2o.cluster_sizes | Retrieve the cluster sizes | |
| h2o.import_sql_table | Import SQL Table into H2O | |
| h2o.coef | Return the coefficients that can be applied to the non-standardized data. | |
| h2o.ceiling | Take a single numeric argument and return a numeric vector with the smallest integers | |
| h2o.arrange | Sorts an H2O frame by columns | |
| h2o.asfactor | Convert H2O Data to Factors | |
| h2o.cross_validation_models | Retrieve the cross-validation models | |
| h2o.clusterIsUp | Determine if an H2O cluster is up or not | |
| h2o.cummin | Return the cumulative min over a column or across a row | |
| h2o.describe | H2O Description of A Dataset | |
| h2o.anyFactor | Check H2OFrame columns for factors | |
| h2o.grid | H2O Grid Support | |
| h2o.cosh | Compute the hyperbolic cosine of x | |
| h2o.asnumeric | Convert H2O Data to Numerics | |
| h2o.coxph | Trains a Cox Proportional Hazards Model (CoxPH) on an H2O dataset | |
| h2o.cos | Compute the cosine of x | |
| h2o.cumprod | Return the cumulative product over a column or across a row | |
| h2o.cor | Correlation of columns. | |
| h2o.downloadAllLogs | Download H2O Log Files to Disk | |
| h2o.clusterStatus | Return the status of the cluster | |
| h2o.cross_validation_predictions | Retrieve the cross-validation predictions | |
| h2o.cummax | Return the cumulative max over a column or across a row | |
| h2o.confusionMatrix | Access H2O Confusion Matrices | |
| h2o.list_api_extensions | List registered API extensions | |
| h2o.isax | iSAX | |
| h2o.list_core_extensions | List registered core extensions | |
| h2o.dct | Compute DCT of an H2OFrame | |
| h2o.entropy | Shannon entropy | |
| h2o.downloadCSV | Download H2O Data to Disk | |
| h2o.computeGram | Compute weighted gram matrix. | |
| h2o.getGrid | Get a grid object from H2O distributed K/V store. | |
| h2o.hit_ratio_table | Retrieve the Hit Ratios | |
| h2o.get_segment_models | Retrieves an instance of H2OSegmentModels for a given id. | |
| h2o.deepfeatures | Feature Generation via H2O Deep Learning | |
| h2o.quantile | Quantiles of H2O Frames. | |
| h2o.decryptionSetup | Setup a Decryption Tool | |
| h2o.connect | Connect to a running H2O instance. | |
| h2o.filterNACols | Filter NA Columns | |
| h2o.ddply | Split H2O Dataset, Apply Function, and Return Results | |
| h2o.make_metrics | Create Model Metrics from predicted and actual values in H2O | |
| h2o.list_jobs | Return list of jobs performed by the H2O cluster | |
| h2o.match | Value Matching in H2O | |
| 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.dayOfWeek | Convert Milliseconds to Day of Week in H2O Datasets | |
| h2o.day | Convert Milliseconds to Day of Month in H2O Datasets | |
| h2o.gam | Fit a General Additive Model | |
| h2o.head | Return the Head or Tail of an H2O Dataset. | |
| h2o.deeplearning | Build a Deep Neural Network model using CPUs | |
| h2o.mean_residual_deviance | Retrieve the Mean Residual Deviance value | |
| h2o.loadGrid | Loads previously saved grid with all it's models from the same folder | |
| h2o.impute | Basic Imputation of H2O Vectors | |
| h2o.dimnames | Column names of an H2OFrame | |
| h2o.findSynonyms | Find synonyms using a word2vec model. | |
| h2o.distance | Compute a pairwise distance measure between all rows of two numeric H2OFrames. | |
| 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.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.glm | Fit a generalized linear model | |
| h2o.glrm | Generalized low rank decomposition of an H2O data frame | |
| h2o.download_mojo | Download the model in MOJO format. | |
| h2o.gbm | Build gradient boosted classification or regression trees | |
| h2o.median | H2O Median | |
| h2o.getVersion | Get h2o version | |
| h2o.ifelse | H2O Apply Conditional Statement | |
| h2o.isolationForest | Trains an Isolation Forest model | |
| h2o.exportFile | Export an H2O Data Frame (H2OFrame) to a File or to a collection of Files. | |
| h2o.exp | Compute the exponential function of x | |
| h2o.importFile | Import Files into H2O | |
| h2o.mojo_predict_df | H2O Prediction from R without having H2O running | |
| h2o.kmeans | Performs k-means clustering on an H2O dataset | |
| h2o.hour | Convert Milliseconds to Hour of Day in H2O Datasets | |
| h2o.getId | Get back-end distributed key/value store id from an H2OFrame. | |
| h2o.giniCoef | Retrieve the GINI Coefficcient | |
| h2o.melt | Converts a frame to key-value representation while optionally skipping NA values. Inverse operation to h2o.pivot. | |
| h2o.getConnection | Retrieve an H2O Connection | |
| h2o.merge | Merge Two H2O Data Frames | |
| h2o.grep | Search for matches to an argument pattern | |
| h2o.keyof | Method on Keyed objects allowing to obtain their key. | |
| h2o.isnumeric | Check if numeric | |
| h2o.hist | Compute A Histogram | |
| h2o.get_automl | Get an R object that is a subclass of H2OAutoML | |
| h2o.kfold_column | Produce a k-fold column vector. | |
| h2o.find_row_by_threshold | Find the threshold, give the max metric. No duplicate thresholds allowed | |
| h2o.killMinus3 | Dump the stack into the JVM's stdout. | |
| h2o.partialPlot | Partial Dependence Plots | |
| h2o.find_threshold_by_max_metric | Find the threshold, give the max metric | |
| h2o.ischaracter | Check if character | |
| h2o.init | Initialize and Connect to H2O | |
| h2o.ls | List Keys on an H2O Cluster | |
| h2o.std_coef_plot | Plot Standardized Coefficient Magnitudes | |
| h2o.getTypes | Get the types-per-column | |
| h2o.getTimezone | Get the Time Zone on the H2O cluster Returns a string | |
| h2o.logloss | Retrieve the Log Loss Value | |
| h2o.print | Print An H2OFrame | |
| h2o.group_by | Group and Apply by Column | |
| h2o.mse | Retrieves Mean Squared Error Value | |
| h2o.log2 | Compute the log2 of x | |
| h2o.ncol | Return the number of columns present in x. | |
| h2o.networkTest | View Network Traffic Speed | |
| h2o.removeVecs | Delete Columns from an H2OFrame | |
| h2o.removeAll | Remove All Objects on the H2O Cluster | |
| h2o.na_omit | Remove Rows With NAs | |
| h2o.max | Returns the maxima of the input values. | |
| h2o.performance | Model Performance Metrics in H2O | |
| h2o.prod | Return the product of all the values present in its arguments. | |
| h2o.nacnt | Count of NAs per column | |
| h2o.nrow | Return the number of rows present in x. | |
| h2o.isfactor | Check if factor | |
| h2o.residual_deviance | Retrieve the residual deviance | |
| h2o.range | Returns a vector containing the minimum and maximum of all the given arguments. | |
| h2o.num_iterations | Retrieve the number of iterations. | |
| h2o.naiveBayes | Compute naive Bayes probabilities on an H2O dataset. | |
| h2o.null_dof | Retrieve the null degrees of freedom | |
| h2o.randomForest | Build a Random Forest model | |
| h2o.gsub | String Global Substitute | |
| h2o.kurtosis | Kurtosis of a column | |
| h2o.levels | Return the levels from the column requested column. | |
| h2o.startLogging | Start Writing H2O R Logs | |
| h2o.logAndEcho | Log a message on the server-side logs | |
| h2o.saveGrid | Saves an existing Grid of models into a given folder. | |
| h2o.rmse | Retrieves Root Mean Squared Error Value | |
| h2o.rm | Delete Objects In H2O | |
| h2o.residual_dof | Retrieve the residual degrees of freedom | |
| h2o.show_progress | Enable Progress Bar | |
| h2o.rmsle | Retrieve the Root Mean Squared Log Error | |
| h2o.loadModel | Load H2O Model from HDFS or Local Disk | |
| h2o.import_mojo | Imports a MOJO under given path, creating a Generic model with it. | |
| h2o.import_hive_table | Import Hive Table into H2O | |
| h2o.makeGLMModel | Set betas of an existing H2O GLM Model | |
| h2o.num_valid_substrings | Count of substrings >= 2 chars that are contained in file | |
| h2o.set_s3_credentials | Creates a new Amazon S3 client internally with specified credentials. | |
| h2o.runif | Produce a Vector of Random Uniform Numbers | |
| h2o.tanh | Compute the hyperbolic tangent of x | |
| h2o.insertMissingValues | Insert Missing Values into an H2OFrame | |
| h2o.interaction | Categorical Interaction Feature Creation in H2O | |
| h2o.lstrip | Strip set from left | |
| h2o.log | Compute the logarithm of x | |
| h2o.listTimezones | List all of the Time Zones Acceptable by the H2O cluster. | |
| h2o.list_all_extensions | List all H2O registered extensions | |
| h2o.log10 | Compute the log10 of x | |
| h2o.mean_per_class_error | Retrieve the mean per class error | |
| h2o.mean | Compute the frame's mean by-column (or by-row). | |
| h2o.log1p | Compute the log1p of x | |
| h2o.mae | Retrieve the Mean Absolute Error Value | |
| h2o.mktime | Compute msec since the Unix Epoch | |
| h2o.stopLogging | Stop Writing H2O R Logs | |
| h2o.mojo_predict_csv | H2O Prediction from R without having H2O running | |
| h2o.shutdown | Shut Down H2O Instance | |
| h2o.str | Display the structure of an H2OFrame object | |
| h2o.transform | Use H2O Transformation model and apply the underlying transformation | |
| h2o.setTimezone | Set the Time Zone on the H2O cluster | |
| h2o.month | Convert Milliseconds to Months in H2O Datasets | |
| h2o.names | Column names of an H2OFrame | |
| h2o.pivot | Pivot a frame | |
| h2o.sd | Standard Deviation of a column of data. | |
| h2o.nchar | String length | |
| h2o.sum | Compute the frame's sum by-column (or by-row). | |
| h2o.summary | Summarizes the columns of an H2OFrame. | |
| h2o.no_progress | Disable Progress Bar | |
| h2o.parseRaw | H2O Data Parsing | |
| h2o.rep_len | Replicate Elements of Vectors or Lists into H2O | |
| h2o.min | Returns the minima of the input values. | |
| h2o.nlevels | Get the number of factor levels for this frame. | |
| h2o.metric | H2O Model Metric Accessor Functions | |
| h2o.week | Convert Milliseconds to Week of Week Year in H2O Datasets | |
| h2o.null_deviance | Retrieve the null deviance | |
| h2o.proj_archetypes | Convert Archetypes to Features from H2O GLRM Model | |
| h2o.prcomp | Principal component analysis of an H2O data frame | |
| is.h2o | Is H2O Frame object | |
| iris | Edgar Anderson's Iris Data | |
| h2o.target_encode_apply | Apply Target Encoding Map to Frame | |
| h2o.psvm | Trains a Support Vector Machine model on an H2O dataset | |
| h2o.openLog | View H2O R Logs | |
| h2o.target_encode_create | Create Target Encoding Map | |
| h2o.parseSetup | Get a parse setup back for the staged data. | |
| h2o.predict | Predict on an H2O Model | |
| h2o.tolower | Convert strings to lowercase | |
| h2o.rstrip | Strip set from right | |
| h2o.r2 | Retrieve the R2 value | |
| h2o.topN | H2O topN | |
| h2o.strsplit | String Split | |
| 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.unique | H2O Unique | |
| h2o.predict_json | H2O Prediction from R without having H2O running | |
| h2o.relevel | Reorders levels of an H2O factor, similarly to standard R's relevel. | |
| h2o.reconstruct | Reconstruct Training Data via H2O GLRM Model | |
| staged_predict_proba.H2OModel | Predict class probabilities at each stage of an H2O Model | |
| h2o.target_encode_fit | Deprecated API. Please use h2o.targetencoder model instead. | |
| h2o.rbind | Combine H2O Datasets by Rows | |
| h2o.saveMojo | Save an H2O Model Object as Mojo to Disk | |
| h2o.upload_mojo | Imports a MOJO from a local filesystem, creating a Generic model with it. | |
| h2o.transform_word2vec | Transform words (or sequences of words) to vectors using a word2vec 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.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.scoreHistory | Retrieve Model Score History | |
| h2o.var | Variance of a column or covariance of columns. | |
| is.character | Check if character | |
| h2o.sdev | Retrieve the standard deviations of principal components | |
| h2o.scale | Scaling and Centering of an H2OFrame | |
| h2o.setLevels | Set Levels of H2O Factor Column | |
| h2o.stringdist | Compute element-wise string distances between two H2OFrames | |
| h2o.round | Round doubles/floats to the given number of decimal places. | |
| h2o.table | Cross Tabulation and Table Creation in H2O | |
| h2o.saveModel | Save an H2O Model Object to Disk | |
| h2o.signif | Round doubles/floats to the given number of significant digits. | |
| h2o.saveModelDetails | Save an H2O Model Details | |
| h2o.svd | Singular value decomposition of an H2O data frame using the power method | |
| h2o.skewness | Skewness of a column | |
| h2o.which | Which indices are TRUE? | |
| h2o.sin | Compute the sine of x | |
| h2o.xgboost | Build an eXtreme Gradient Boosting model | |
| h2o.splitFrame | Split an H2O Data Set | |
| print.H2OTable | Print method for H2OTable objects | |
| is.factor | Check if factor | |
| h2o.xgboost.available | Determines whether an XGBoost model can be built | |
| str.H2OFrame | Display the structure of an H2OFrame object | |
| predict_leaf_node_assignment.H2OModel | Predict the Leaf Node Assignment on an H2O Model | |
| print.H2OFrame | Print An H2OFrame | |
| plot.H2OTabulate | Plot an H2O Tabulate Heatmap | |
| h2o.sub | String Substitute | |
| h2o.substring | Substring | |
| h2o.train_segments | H2O Segmented-Data Bulk Model Training | |
| h2o.toFrame | Convert a word2vec model into an H2OFrame | |
| h2o.transform,H2OWordEmbeddingModel-method | Transform words (or sequences of words) to vectors using a word2vec model. | |
| predict.H2OAutoML | Predict on an AutoML object | |
| h2o.tokenize | Tokenize String | |
| h2o.varimp_plot | Plot Variable Importances | |
| h2o.transform,H2OTargetEncoderModel-method | Applies target encoding to a given dataset | |
| h2o.tabulate | Tabulation between Two Columns of an H2OFrame | |
| h2o.stackedEnsemble | Builds a Stacked Ensemble | |
| h2o.sqrt | Compute the square root of x | |
| summary,H2OGrid-method | Format grid object in user-friendly way | |
| summary,H2OCoxPHModel-method | Summary method for H2OCoxPHModel objects | |
| h2o.target_encode_transform | Deprecated API. Please use h2o.targetencoder model instead. Transform Frame by Target Encoding Map | |
| h2o.which_max | Which indice contains the max value? | |
| h2o.tan | Compute the tangent of x | |
| h2o.trunc | Truncate values in x toward 0 | |
| h2o.year | Convert Milliseconds to Years in H2O Datasets | |
| housevotes | United States Congressional Voting Records 1984 | |
| is.numeric | Check if numeric | |
| range.H2OFrame | Range of an H2O Column | |
| h2o.weights | Retrieve the respective weight matrix | |
| h2o.targetencoder | Transformation of a categorical variable with a mean value of the target variable | |
| h2o.toupper | Convert strings to uppercase | |
| h2o.varimp | Retrieve the variable importance. | |
| h2o.totss | Get the total sum of squares. | |
| h2o.tot_withinss | Get the total within cluster sum of squares. | |
| names.H2OFrame | Column names of an H2OFrame | |
| h2o.trim | Trim Space | |
| h2o.withinss | Get the Within SS | |
| h2o.which_min | Which index contains the min value? | |
| scale | Scaling and Centering of an H2OFrame | |
| length,H2OTree-method | Overrides the behavior of length() function on H2OTree class. Returns number of nodes in an H2OTree | |
| h2o.word2vec | Trains a word2vec model on a String column of an H2O data frame | |
| summary,H2OModel-method | Print the Model Summary | |
| walking | Muscular Actuations for Walking Subject | |
| plot.H2OModel | Plot an H2O Model | |
| use.package | Use optional package | |
| predict_contributions.H2OModel | Predict feature contributions - SHAP values on an H2O Model (only GBM and XGBoost models). | |
| predict.H2OModel | Predict on an H2O Model | |
| zzz | Shutdown H2O cluster after examples run | |
| prostate | Prostate Cancer Study | |
| H2OCoxPHModel-class | The H2OCoxPHModel object. | |
| Ops.H2OFrame | S3 Group Generic Functions for H2O | |
| H2OGrid-class | H2O Grid | |
| H2OClusteringModel-class | The H2OClusteringModel object. | |
| H2OAutoML-class | The H2OAutoML class | |
| H2OConnection-class | The H2OConnection class. | |
| H2OFrame-class | The H2OFrame class | |
| H2OFrame-Extract | Extract or Replace Parts of an H2OFrame Object | |
| .h2o.__MODEL_METRICS | Model Metrics Endpoint | |
| H2OConnectionMutableState | The H2OConnectionMutableState class | |
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Details
| Type | Package |
| Date | 2020-04-03 |
| License | Apache License (== 2.0) |
| URL | https://github.com/h2oai/h2o-3 |
| BugReports | https://0xdata.atlassian.net/projects/PUBDEV |
| NeedsCompilation | no |
| SystemRequirements | Java (>= 8) |
| Collate | 'aggregator.R' 'astfun.R' 'automl.R' 'classes.R' 'config.R' 'connection.R' 'constants.R' 'datasets.R' 'logging.R' 'communication.R' 'kvstore.R' 'frame.R' 'targetencoder_deprecated.R' 'targetencoder.R' 'import.R' 'isolationforest.R' 'parse.R' 'export.R' 'edicts.R' 'models.R' 'coxph.R' 'coxphutils.R' 'kmeans.R' 'gam.R' 'gbm.R' 'generic.R' 'glm.R' 'glrm.R' 'pca.R' 'svd.R' 'psvm.R' 'deeplearning.R' 'stackedensemble.R' 'xgboost.R' 'randomforest.R' 'naivebayes.R' 'word2vec.R' 'w2vutils.R' 'locate.R' 'grid.R' 'segment.R' 'predict.R' 'zzz.R' |
| RoxygenNote | 7.0.2 |
| Packaged | 2020-04-03 20:51:45 UTC; jenkins |
| Repository | CRAN |
| Date/Publication | 2020-04-09 08:50:02 UTC |
| suggests | bit64 (>= 0.9.7) , data.table (>= 1.9.8) , ggplot2 , Matrix , mlbench , plot3Drgl (>= 1.0.1) , rgl (>= 0.100.19) , slam , survival |
| imports | graphics , jsonlite , RCurl , tools , utils |
| depends | methods , R (>= 2.13.0) , stats |
| Contributors | Amy Wang, Spencer Aiello, Tom Kraljevic, Anqi Fu, Yuan Tang, Jan Gorecki, Matt Dowle, Patrick Aboyoun, H2O.ai, Navdeep Gill, Arno Candel, Cliff Click, Tomas Nykodym, Michal Kurka, Michal Malohlava, Ludi Rehak, Eric Eckstrand, Brandon Hill, Sebastian Vidrio, Surekha Jadhawani, Raymond Peck, Wendy Wong, Lauren DiPerna |
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