h2o v3.32.0.1
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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 (GLM), Gradient Boosting Machines (including XGBoost), Random Forests,
Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes,
Generalized Additive Models (GAM), Cox Proportional Hazards, K-Means, PCA,
Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML).
Functions in h2o
| Name | Description | |
| H2OConnectionMutableState | The H2OConnectionMutableState class | |
| H2OCoxPHModel-class | The H2OCoxPHModel object. | |
| H2OFrame-Extract | Extract or Replace Parts of an H2OFrame Object | |
| H2OClusteringModel-class | The H2OClusteringModel object. | |
| Ops.H2OFrame | S3 Group Generic Functions for H2O | |
| H2OAutoML-class | The H2OAutoML class | |
| H2OCoxPHModelSummary-class | The H2OCoxPHModelSummary object. | |
| H2OConnection-class | The H2OConnection class. | |
| H2OFrame-class | The H2OFrame class | |
| H2OGrid-class | H2O Grid | |
| H2OTree-class | The H2OTree class. | |
| H2OSegmentModels-class | H2O Segment Models | |
| H2ONode-class | The H2ONode class. | |
| H2OSplitNode-class | The H2OSplitNode class. | |
| H2OSegmentModelsFuture-class | H2O Future Segment Models | |
| && | Logical and for H2OFrames | |
| H2OModelMetrics-class | The H2OModelMetrics Object. | |
| H2OModelFuture-class | H2O Future Model | |
| aaa | Starting H2O For examples | |
| .h2o.__DECRYPTION_SETUP | Decryption Endpoints | |
| Logical-or | Logical or for H2OFrames | |
| apply | Apply on H2O Datasets | |
| H2OLeafNode-class | The H2OLeafNode class. | |
| .h2o.__PARSE_SETUP | Parse Endpoints | |
| .h2o.__DKV | Removal Endpoints | |
| ModelAccessors | Accessor Methods for H2OModel Object | |
| australia | Australia Coastal Data | |
| .get_algorithm | Get the algoritm used by the model_or_model_id | |
| .find_appropriate_column_name | Tries to match a fuzzy_col_name with a column name that exists in cols. | |
| .h2o.doGET | Just like doRawGET but fills in the default h2oRestApiVersion if none is provided | |
| as.vector.H2OFrame | Convert an H2OFrame to a vector | |
| .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. | |
| .h2o.perfect_auc | Internal function that calculates a precise AUC from given probabilities and actual responses. | |
| .h2o.primitives | Map of operations known to H2O | |
| .h2o.doPOST | Just like doRawPOST but fills in the default h2oRestApiVersion if none is provided | |
| .h2o.__RAPIDS | Rapids Endpoint | |
| as.character.H2OFrame | Convert an H2OFrame to a String | |
| .h2o.__REST_API_VERSION | H2O Package Constants | |
| .consolidate_varimps | Consolidate variable importances | |
| .customized_call | A helper function that makes it easier to override/add params in a function call. | |
| .create_leaderboard | Create a leaderboard like data frame for models | |
| .collapse | Helper Collapse Function | |
| .h2o.__MODEL_BUILDERS | Model Builder Endpoint Generator | |
| Keyed-class | Virtual Keyed class | |
| colnames | Returns the column names of an H2OFrame | |
| as.h2o | Create H2OFrame | |
| as.factor | Convert H2O Data to Factors | |
| H2OModel-class | The H2OModel object. | |
| .h2o.__SEGMENT_MODELS_BUILDERS | Segment Models Builder Endpoint Generator | |
| as.data.frame.H2OFrame | Converts parsed H2O data into an R data frame | |
| h2o.asnumeric | Convert H2O Data to Numerics | |
| .get_feature_count | Get feature count sorted by the count descending. | |
| .get_first_of_family | Get first of family models | |
| dim.H2OFrame | Returns the Dimensions of an H2OFrame | |
| .h2o.doRawPOST | Perform a low-level HTTP POST operation on an H2O instance | |
| .h2o.__FRAMES | Inspect/Summary Endpoints | |
| .h2o.__EXPORT_FILES | Export Files Endpoint Generator | |
| .h2o.doRawGET | Perform a low-level HTTP GET operation on an H2O instance | |
| as.matrix.H2OFrame | Convert an H2OFrame to a matrix | |
| as.data.frame.H2OSegmentModels | Converts a collection of Segment Models to a data.frame | |
| as.numeric | Convert H2O Data to Numeric | |
| dimnames.H2OFrame | Column names of an H2OFrame | |
| h2o.assign | Rename an H2O object. | |
| .addParm | TODO: No objects in this file are being used. Either remove file or use objects. | |
| .interpretable | Is the model considered to be interpretable, i.e., simple enough. | |
| .has_varimp | Has the model variable importance? | |
| .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. | |
| .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. | |
| .varimp | Get variable importance in a standardized way. | |
| .h2o.__MODEL_METRICS | Model Metrics Endpoint | |
| h2o.aucpr | Retrieve the AUCPR (Area Under Precision Recall Curve) | |
| h2o.auc | Retrieve the AUC | |
| .h2o.__LOGANDECHO | Log and Echo Endpoint | |
| .is_plotting_to_rnotebook | Check if we are plotting in to r notebook. | |
| .leaderboard_for_row | Enhance leaderboard with per-model predictions. | |
| .h2o.__MODELS | Model Endpoint | |
| .is_h2o_model | Is the model an H2O model? | |
| .plot_varimp | Plot variable importances with ggplot2 | |
| h2o.abs | Compute the absolute value of x | |
| h2o.HGLMMetrics | Retrieve HGLM ModelMetrics | |
| h2o.arrange | Sorts an H2O frame by columns | |
| h2o.as_date | Convert between character representations and objects of Date class | |
| .pkg.env | The H2O Package Environment | |
| .is_h2o_tree_model | Is the model a Tree-based H2O Model? | |
| .h2o.__ALL_CAPABILITIES | Capabilities endpoints | |
| .h2o.__IMPORT | Import/Export Endpoints | |
| .h2o.__CREATE_FRAME | H2OFrame Manipulation | |
| .h2o.__JOBS | Administrative Endpoints | |
| h2o.ceiling | Take a single numeric argument and return a numeric vector with the smallest integers | |
| h2o.cross_validation_holdout_predictions | Retrieve the cross-validation holdout predictions | |
| h2o.cumsum | Return the cumulative sum over a column or across a row | |
| h2o.cbind | Combine H2O Datasets by Columns | |
| h2o.cross_validation_fold_assignment | Retrieve the cross-validation fold assignment | |
| h2o.cumprod | Return the cumulative product over a column or across a row | |
| .shorten_model_ids | Shortens model ids if possible (iff there will be same amount of unique model_ids as before) | |
| .h2o.__W2V_SYNONYMS | Word2Vec Endpoints | |
| generate_col_ind | CHeck to see if the column names/indices entered is valid for the dataframe given. This is an internal function | |
| feature_frequencies.H2OModel | Retrieve the number of occurrences of each feature for given observations Available for GBM, Random Forest and Isolation Forest models. | |
| h2o.clusterInfo | Print H2O cluster info | |
| h2o.anomaly | Anomaly Detection via H2O Deep Learning Model | |
| h2o.createFrame | Data H2OFrame Creation in H2O | |
| h2o.clearLog | Delete All H2O R Logs | |
| h2o.centroid_stats | Retrieve centroid statistics | |
| h2o.coxph | Trains a Cox Proportional Hazards Model (CoxPH) on an H2O dataset | |
| h2o.all | Given a set of logical vectors, are all of the values true? | |
| h2o.dayOfWeek | Convert Milliseconds to Day of Week in H2O Datasets | |
| h2o.dct | Compute DCT of an H2OFrame | |
| h2o.clusterIsUp | Determine if an H2O cluster is up or not | |
| h2o.cross_validation_models | Retrieve the cross-validation models | |
| h2o.acos | Compute the arc cosine of x | |
| .h2o.is_progress | Check if Progress Bar is Enabled | |
| .min_max | Min-max normalization. | |
| .h2o.__checkConnectionHealth | Check H2O Server Health | |
| .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 | |
| .verify_dataxy | Used to verify data, x, y and turn into the appropriate things | |
| h2o.centersSTD | Retrieve the Model Centers STD | |
| h2o-package | H2O R Interface | |
| h2o.centers | Retrieve the Model Centers | |
| 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.describe | H2O Description of A Dataset | |
| h2o.aggregated_frame | Retrieve an aggregated frame from an Aggregator model | |
| h2o.downloadAllLogs | Download H2O Log Files to Disk | |
| h2o.distance | Compute a pairwise distance measure between all rows of two numeric H2OFrames. | |
| h2o.cross_validation_predictions | Retrieve the cross-validation predictions | |
| h2o.find_row_by_threshold | Find the threshold, give the max metric. No duplicate thresholds allowed | |
| h2o.gbm | Build gradient boosted classification or regression trees | |
| h2o.drop_duplicates | Drops duplicated rows. | |
| h2o.difflag1 | Conduct a lag 1 transform on a numeric H2OFrame column | |
| h2o.entropy | Shannon entropy | |
| h2o.find_threshold_by_max_metric | Find the threshold, give the max metric | |
| .model_ids | Get Model Ids | |
| h2o.asfactor | Convert H2O Data to Factors | |
| h2o.biases | Return the respective bias vector | |
| h2o.ascharacter | Convert H2O Data to Characters | |
| h2o.downloadCSV | Download H2O Data to Disk | |
| h2o.get_segment_models | Retrieves an instance of H2OSegmentModels for a given id. | |
| h2o.giniCoef | Retrieve the GINI Coefficcient | |
| 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.getGrid | Get a grid object from H2O distributed K/V store. | |
| h2o.floor | Take a single numeric argument and return a numeric vector with the largest integers | |
| h2o.flow | Open H2O Flow | |
| 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.aic | Retrieve the Akaike information criterion (AIC) value | |
| h2o.aggregator | Build an Aggregated Frame | |
| h2o.bottomN | H2O bottomN | |
| h2o.columns_by_type | Obtain a list of columns that are specified by `coltype` | |
| h2o.colnames | Return column names of an H2OFrame | |
| h2o.grep | Search for matches to an argument pattern | |
| h2o.anyFactor | Check H2OFrame columns for factors | |
| h2o.any | Given a set of logical vectors, is at least one of the values true? | |
| h2o.automl | Automatic Machine Learning | |
| h2o.betweenss | Get the between cluster sum of squares | |
| h2o.getTimezone | Get the Time Zone on the H2O cluster Returns a string | |
| h2o.getId | Get back-end distributed key/value store id from an H2OFrame. | |
| h2o.decryptionSetup | Setup a Decryption Tool | |
| h2o.ddply | Split H2O Dataset, Apply Function, and Return Results | |
| h2o.dim | Returns the number of rows and columns for an H2OFrame object. | |
| h2o.cos | Compute the cosine of x | |
| h2o.cosh | Compute the hyperbolic cosine of x | |
| h2o.exportHDFS | Export a Model to HDFS | |
| h2o.isfactor | Check if factor | |
| h2o.hit_ratio_table | Retrieve the Hit Ratios | |
| h2o.hour | Convert Milliseconds to Hour of Day in H2O Datasets | |
| h2o.getTypes | Get the types-per-column | |
| h2o.clusterStatus | Return the status of the cluster | |
| h2o.download_mojo | Download the model in MOJO format. | |
| h2o.dimnames | Column names of an H2OFrame | |
| h2o.download_pojo | Download the Scoring POJO (Plain Old Java Object) of an H2O Model | |
| h2o.cor | Correlation of columns. | |
| h2o.cummin | Return the cumulative min over a column or across a row | |
| 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.connect | Connect to a running H2O instance. | |
| h2o.cummax | Return the cumulative max over a column or across a row | |
| h2o.coef | Return the coefficients that can be applied to the non-standardized data. | |
| h2o.deepfeatures | Feature Generation via H2O Deep Learning | |
| h2o.cluster_sizes | Retrieve the cluster sizes | |
| h2o.filterNACols | Filter NA Columns | |
| h2o.isnumeric | Check if numeric | |
| h2o.grid | H2O Grid Support | |
| h2o.list_core_extensions | List registered core extensions | |
| h2o.max | Returns the maxima of the input values. | |
| h2o.list_api_extensions | List registered API extensions | |
| h2o.match | Value Matching in H2O | |
| h2o.openLog | View H2O R Logs | |
| h2o.parseRaw | H2O Data Parsing | |
| h2o.range | Returns a vector containing the minimum and maximum of all the given arguments. | |
| h2o.residual_analysis_plot | Residual Analysis | |
| 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.importFile | Import Files into H2O | |
| h2o.computeGram | Compute weighted gram matrix. | |
| h2o.findSynonyms | Find synonyms using a word2vec model. | |
| h2o.glrm | Generalized low rank decomposition of an H2O data frame | |
| h2o.import_sql_table | Import SQL Table into H2O | |
| h2o.glm | Fit a generalized linear model | |
| h2o.genericModel | Imports a model under given path, creating a Generic model with it. | |
| h2o.exp | Compute the exponential function of x | |
| h2o.explain | Generate Model Explanations | |
| h2o.getVersion | Get h2o version | |
| h2o.deeplearning | Build a Deep Neural Network model using CPUs | |
| h2o.getConnection | Retrieve an H2O Connection | |
| h2o.get_automl | Get an R object that is a subclass of H2OAutoML | |
| h2o.ice_plot | Plot Individual Conditional Expectation (ICE) for each decile | |
| h2o.residual_deviance | Retrieve the residual deviance | |
| h2o.import_hive_table | Import Hive Table into H2O | |
| h2o.confusionMatrix | Access H2O Confusion Matrices | |
| h2o.logloss | Retrieve the Log Loss Value | |
| h2o.list_jobs | Return list of jobs performed by the H2O cluster | |
| h2o.kfold_column | Produce a k-fold column vector. | |
| h2o.ls | List Keys on an H2O Cluster | |
| h2o.loadGrid | Loads previously saved grid with all it's models from the same folder | |
| h2o.impute | Basic Imputation of H2O Vectors | |
| h2o.killMinus3 | Dump the stack into the JVM's stdout. | |
| h2o.month | Convert Milliseconds to Months in H2O Datasets | |
| h2o.residual_dof | Retrieve the residual degrees of freedom | |
| h2o.kolmogorov_smirnov | Kolmogorov-Smirnov metric for binomial models | |
| h2o.ifelse | H2O Apply Conditional Statement | |
| h2o.rm | Delete Objects In H2O | |
| h2o.kmeans | Performs k-means clustering on an H2O dataset | |
| h2o.loadModel | Load H2O Model from HDFS or Local Disk | |
| h2o.mse | Retrieves Mean Squared Error Value | |
| h2o.day | Convert Milliseconds to Day of Month in H2O Datasets | |
| h2o.cut | Cut H2O Numeric Data to Factor | |
| h2o.explain_row | Generate Model Explanations for a single row | |
| h2o.scale | Scaling and Centering of an H2OFrame | |
| h2o.log2 | Compute the log2 of x | |
| h2o.metric | H2O Model Metric Accessor Functions | |
| h2o.logAndEcho | Log a message on the server-side logs | |
| h2o.log | Compute the logarithm of x | |
| h2o.min | Returns the minima of the input values. | |
| h2o.gainsLift | Access H2O Gains/Lift Tables | |
| h2o.exportFile | Export an H2O Data Frame (H2OFrame) to a File or to a collection of Files. | |
| h2o.init | Initialize and Connect to H2O | |
| h2o.insertMissingValues | Insert Missing Values into an H2OFrame | |
| h2o.networkTest | View Network Traffic Speed | |
| h2o.mktime | Compute msec since the Unix Epoch | |
| 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.gam | Fit a General Additive Model | |
| h2o.get_leaderboard | Retrieve the leaderboard from the AutoML instance. | |
| h2o.getFrame | Get an R Reference to an H2O Dataset, that will NOT be GC'd by default | |
| h2o.fillna | fillNA | |
| h2o.getGLMFullRegularizationPath | Extract full regularization path from a GLM model | |
| h2o.nchar | String length | |
| h2o.ncol | Return the number of columns present in x. | |
| h2o.model_correlation_heatmap | Model Prediction Correlation Heatmap | |
| h2o.psvm | Trains a Support Vector Machine model on an H2O dataset | |
| h2o.performance | Model Performance Metrics in H2O | |
| h2o.nlevels | Get the number of factor levels for this frame. | |
| h2o.isolationForest | Trains an Isolation Forest model | |
| h2o.gsub | String Global Substitute | |
| h2o.import_sql_select | Import SQL table that is result of SELECT SQL query into H2O | |
| h2o.list_all_extensions | List all H2O registered extensions | |
| h2o.listTimezones | List all of the Time Zones Acceptable by the H2O cluster. | |
| h2o.group_by | Group and Apply by Column | |
| h2o.import_mojo | Imports a MOJO under given path, creating a Generic model with it. | |
| h2o.lstrip | Strip set from left | |
| h2o.rep_len | Replicate Elements of Vectors or Lists into H2O | |
| h2o.quantile | Quantiles of H2O Frames. | |
| h2o.mae | Retrieve the Mean Absolute Error Value | |
| h2o.pivot | Pivot a frame | |
| h2o.saveModelDetails | Save an H2O Model Details | |
| h2o.round | Round doubles/floats to the given number of decimal places. | |
| h2o.skewness | Skewness of a column | |
| h2o.rstrip | Strip set from right | |
| h2o.reset_threshold | Reset model threshold and return old threshold value. | |
| h2o.saveMojo | Deprecated - use h2o.save_mojo instead. Save an H2O Model Object as Mojo to Disk | |
| h2o.get_ntrees_actual | Retrieve actual number of trees for tree algorithms | |
| h2o.hist | Compute A Histogram | |
| h2o.head | Return the Head or Tail of an H2O Dataset. | |
| h2o.scoreHistory | Retrieve Model Score History | |
| h2o.interaction | Categorical Interaction Feature Creation in H2O | |
| h2o.print | Print An H2OFrame | |
| h2o.predict_json | H2O Prediction from R without having H2O running | |
| h2o.is_client | Check Client Mode Connection | |
| h2o.ischaracter | Check if character | |
| h2o.isax | iSAX | |
| h2o.sdev | Retrieve the standard deviations of principal components | |
| h2o.rulefit | Build a RuleFit Model | |
| h2o.runif | Produce a Vector of Random Uniform Numbers | |
| h2o.setLevels | Set Levels of H2O Factor Column | |
| h2o.shutdown | Shut Down H2O Instance | |
| h2o.show_progress | Enable Progress Bar | |
| h2o.log10 | Compute the log10 of x | |
| h2o.stringdist | Compute element-wise string distances between two H2OFrames | |
| h2o.keyof | Method on Keyed objects allowing to obtain their key. | |
| h2o.totss | Get the total sum of squares. | |
| h2o.splitFrame | Split an H2O Data Set | |
| h2o.toupper | Convert strings to uppercase | |
| h2o.upload_mojo | Imports a MOJO from a local filesystem, creating a Generic model with it. | |
| h2o.xgboost | Build an eXtreme Gradient Boosting 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.) | |
| model_cache-class | Needed to be able to memoise the models | |
| summary,H2OAutoML-method | Format AutoML object in user-friendly way | |
| names.H2OFrame | Column names of an H2OFrame | |
| h2o.xgboost.available | Determines whether an XGBoost model can be built | |
| h2o.kurtosis | Kurtosis of a column | |
| h2o.mean_residual_deviance | Retrieve the Mean Residual Deviance value | |
| h2o.strsplit | String Split | |
| h2o.levels | Return the levels from the column requested column. | |
| h2o.mojo_predict_csv | H2O Prediction from R without having H2O running | |
| h2o.median | H2O Median | |
| h2o.mojo_predict_df | H2O Prediction from R without having H2O running | |
| h2o.na_omit | Remove Rows With NAs | |
| h2o.mean | Compute the frame's mean by-column (or by-row). | |
| h2o.log1p | Compute the log1p of x | |
| h2o.mean_per_class_error | Retrieve the mean per class error | |
| summary,H2OCoxPHModel-method | Summary method for H2OCoxPHModel objects | |
| h2o.sin | Compute the sine of x | |
| h2o.str | Display the structure of an H2OFrame object | |
| h2o.signif | Round doubles/floats to the given number of significant digits. | |
| h2o.stopLogging | Stop Writing H2O R Logs | |
| h2o.svd | Singular value decomposition of an H2O data frame using the power method | |
| h2o.num_iterations | Retrieve the number of iterations. | |
| h2o.num_valid_substrings | Count of substrings >= 2 chars that are contained in file | |
| h2o.pd_multi_plot | Plot partial dependencies for a variable across multiple models | |
| h2o.r2 | Retrieve the R2 value | |
| h2o.pd_plot | Plot partial dependence for a variable | |
| h2o.nacnt | Count of NAs per column | |
| h2o.null_dof | Retrieve the null degrees of freedom | |
| h2o.partialPlot | Partial Dependence Plots | |
| h2o.parseSetup | Get a parse setup back for the staged data. | |
| h2o.prod | Return the product of all the values present in its arguments. | |
| h2o.null_deviance | Retrieve the null deviance | |
| h2o.makeGLMModel | Set betas of an existing H2O GLM Model | |
| h2o.make_metrics | Create Model Metrics from predicted and actual values in H2O | |
| 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.proj_archetypes | Convert Archetypes to Features from H2O GLRM Model | |
| h2o.rapids | Execute a Rapids expression. | |
| h2o.table | Cross Tabulation and Table Creation in H2O | |
| h2o.reconstruct | Reconstruct Training Data via H2O GLRM Model | |
| h2o.relevel | Reorders levels of an H2O factor, similarly to standard R's relevel. | |
| h2o.randomForest | Build a Random Forest model | |
| h2o.transform,H2OTargetEncoderModel-method | Applies target encoding to a given dataset | |
| h2o.unique | H2O Unique | |
| h2o.train_segments | H2O Segmented-Data Bulk Model Training | |
| h2o.trunc | Truncate values in x toward 0 | |
| h2o.varimp_heatmap | Variable Importance Heatmap across multiple models | |
| h2o.rbind | Combine H2O Datasets by Rows | |
| h2o.rmse | Retrieves Root Mean Squared Error Value | |
| h2o.varimp_plot | Plot Variable Importances | |
| h2o.tabulate | Tabulation between Two Columns of an H2OFrame | |
| h2o.tan | Compute the tangent of x | |
| h2o.topN | H2O topN | |
| h2o.saveModel | Save an H2O Model Object to Disk | |
| h2o.save_to_hive | Save contents of this data frame into a Hive table | |
| h2o.saveGrid | Saves an existing Grid of models into a given folder. | |
| h2o.save_mojo | Save an H2O Model Object as Mojo to Disk | |
| is.factor | Check if factor | |
| is.h2o | Is H2O Frame object | |
| h2o.names | Column names of an H2OFrame | |
| h2o.nrow | Return the number of rows present in x. | |
| h2o.naiveBayes | Compute naive Bayes probabilities on an H2O dataset. | |
| h2o.no_progress | Disable Progress Bar | |
| h2o.tot_withinss | Get the total within cluster sum of squares. | |
| h2o.rmsle | Retrieve the Root Mean Squared Log Error | |
| h2o.predict | Predict on an H2O Model | |
| h2o.prcomp | Principal component analysis of an H2O data frame | |
| h2o.removeAll | Remove All Objects on the H2O Cluster | |
| h2o.removeVecs | Delete Columns from an H2OFrame | |
| h2o.set_s3_credentials | Creates a new Amazon S3 client internally with specified credentials. | |
| h2o.setTimezone | Set the Time Zone on the H2O cluster | |
| h2o.startLogging | Start Writing H2O R Logs | |
| h2o.sum | Compute the frame's sum by-column (or by-row). | |
| h2o.std_coef_plot | Plot Standardized Coefficient Magnitudes | |
| h2o.summary | Summarizes the columns of an H2OFrame. | |
| h2o.sqrt | Compute the square root of x | |
| predict_leaf_node_assignment.H2OModel | Predict the Leaf Node Assignment on an H2O Model | |
| predict_contributions.H2OModel | Predict feature contributions - SHAP values on an H2O Model (only DRF, GBM and XGBoost models). | |
| summary,H2OGrid-method | Format grid object in user-friendly way | |
| h2o.sd | Standard Deviation of a column of data. | |
| h2o.scoreHistoryGAM | Retrieve GLM Model Score History buried in GAM model | |
| h2o.tokenize | Tokenize String | |
| h2o.var | Variance of a column or covariance of columns. | |
| h2o.tolower | Convert strings to lowercase | |
| h2o.varimp | Retrieve the variable importance. | |
| summary,H2OModel-method | Print the Model Summary | |
| h2o.shap_summary_plot | SHAP Summary Plot | |
| h2o.shap_explain_row_plot | SHAP Local Explanation | |
| h2o.sub | String Substitute | |
| h2o.transform,H2OWordEmbeddingModel-method | Transform words (or sequences of words) to vectors using a word2vec model. | |
| prostate | Prostate Cancer Study | |
| 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.) | |
| is.numeric | Check if numeric | |
| length,H2OTree-method | Overrides the behavior of length() function on H2OTree class. Returns number of nodes in an H2OTree | |
| h2o.week | Convert Milliseconds to Week of Week Year in H2O Datasets | |
| range.H2OFrame | Range of an H2O Column | |
| h2o.transform | Use H2O Transformation model and apply the underlying transformation | |
| h2o.target_encode_create | Create Target Encoding Map | |
| h2o.stackedEnsemble | Builds a Stacked Ensemble | |
| h2o.targetencoder | Transformation of a categorical variable with a mean value of the target variable | |
| h2o.withinss | Get the Within SS | |
| h2o.which | Which indices are TRUE? | |
| scale | Scaling and Centering of an H2OFrame | |
| predict.H2OAutoML | Predict on an AutoML object | |
| h2o.weights | Retrieve the respective weight matrix | |
| iris | Edgar Anderson's Iris Data | |
| predict.H2OModel | Predict on an H2O Model | |
| is.character | Check if character | |
| with_no_h2o_progress | Suppresses h2o progress output from expr | |
| plot.H2OModel | Plot an H2O Model | |
| h2o.word2vec | Trains a word2vec model on a String column of an H2O data frame | |
| zzz | Shutdown H2O cluster after examples run | |
| plot.H2OTabulate | Plot an H2O Tabulate Heatmap | |
| show,H2OAutoML-method | Format AutoML object in user-friendly way | |
| h2o.tanh | Compute the hyperbolic tangent of x | |
| h2o.target_encode_apply | Apply Target Encoding Map to Frame | |
| h2o.substring | Substring | |
| h2o.tf_idf | Computes TF-IDF values for each word in given documents. | |
| h2o.transform_word2vec | Transform words (or sequences of words) to vectors using a word2vec model. | |
| h2o.toFrame | Convert a word2vec model into an H2OFrame | |
| h2o.trim | Trim Space | |
| h2o.which_max | Which indice contains the max value? | |
| h2o.which_min | Which index contains the min value? | |
| h2o.year | Convert Milliseconds to Years in H2O Datasets | |
| print.H2OFrame | Print An H2OFrame | |
| staged_predict_proba.H2OModel | Predict class probabilities at each stage of an H2O Model | |
| housevotes | United States Congressional Voting Records 1984 | |
| print.H2OTable | Print method for H2OTable objects | |
| use.package | Use optional package | |
| str.H2OFrame | Display the structure of an H2OFrame object | |
| walking | Muscular Actuations for Walking Subject | |
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Details
| Type | Package |
| Date | 2020-10-08 |
| 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.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' 'tf-idf.R' 'rulefit.R' 'explain.R' 'zzz.R' |
| RoxygenNote | 7.1.1 |
| Packaged | 2020-10-08 18:20:22 UTC; jenkins |
| Repository | CRAN |
| Date/Publication | 2020-10-17 07:00:02 UTC |
| suggests | bit64 (>= 0.9.7) , data.table (>= 1.9.8) , DT , ggplot2 (>= 3.3.0) , htmltools , IRdisplay , Matrix , mlbench , plot3Drgl (>= 1.0.1) , plotly , repr , 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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