Package: catboostr 1.2.10-1

Dennis Alexis Valin Dittrich

catboostr: Gradient Boosting on Decision Trees

Open-source gradient boosting on decision trees with categorical features support out of the box. A fork of the upstream CatBoost R package packaged for CRAN-style installation; the R API is a strict superset of upstream's catboost.* interface.

Authors:Dennis Alexis Valin Dittrich [cre, aut], Nikita Dmitriev [aut], Andrey Gulin [aut], Anna Veronika Dorogush [aut], Stanislav Kirillov [aut], Victor Omelyanenko [aut], Evgueni Petrov [aut], Andrey Mishenko [aut], Pavel Kalinin [aut], Vyacheslav Alipov [aut], Egor Samosvat [aut], Alexander Kobotov [aut], Dmitry Schelchkov [aut], Vasily Ershov [aut], Vyacheslav Murashkin [aut], Roman Nefyodov [aut], Anastasia Bezzubtseva [aut], Alexey Gorchakov [aut], Ivan Karev [aut], Sergey Berezhnoy [aut], Roman Rybalchenko [aut], Valeriy Babushkin [aut], Mikhail Pershin [aut], Emil Kayumov [aut], Evgeny Eltyshev [aut], Alexander Baranov [aut], Dmitry Oganesyan [aut], Dmitry Baksheev [aut], CatBoost DevTeam [aut, cph], WLOG Solutions [ctb]

catboostr_1.2.10-1.tar.gz
catboostr_1.2.10-1.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
catboostr/json (API)

# Install 'catboostr' in R:
install.packages('catboostr', repos = c('https://davdittrich.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/davdittrich/catboostr/issues

On CRAN:

Conda:

boostingcatboostcategorical-featurescmakecppdata-sciencedecision-treesensemble-learninggbdtgradient-boostinggradient-boosting-decision-treesmachine-learningpredictive-modelingtree-ensemble

3.18 score 101 scripts 109 exports 1 dependencies

Last updated from:15b2b989c2. Checks:14 FAIL, 1 OK. Indexed: yes.

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Exports:catboost.calc_feature_statisticscatboost.calc_leaf_indexescatboost.caretcatboost.comparecatboost.compute_training_optionscatboost.create_metric_calcercatboost.cvcatboost.dataset_statisticscatboost.Dictionarycatboost.dictionary.applycatboost.dictionary.end_of_sentence_token_idcatboost.dictionary.fitcatboost.dictionary.get_tokencatboost.dictionary.get_tokenscatboost.dictionary.get_top_tokenscatboost.dictionary.loadcatboost.dictionary.min_unused_token_idcatboost.dictionary.savecatboost.dictionary.sizecatboost.dictionary.unknown_token_idcatboost.drop_unused_featurescatboost.eval_featurecatboost.eval_metriccatboost.eval_metricscatboost.features_data.get_cat_feature_countcatboost.features_data.get_feature_countcatboost.features_data.get_feature_namescatboost.features_data.get_num_feature_countcatboost.features_data.get_object_countcatboost.FeaturesDatacatboost.get_best_iterationcatboost.get_best_scorecatboost.get_borderscatboost.get_evals_resultcatboost.get_feature_importancecatboost.get_leaf_valuescatboost.get_leaf_weightscatboost.get_metadatacatboost.get_model_feature_namescatboost.get_model_paramscatboost.get_object_importancecatboost.get_plain_paramscatboost.get_probability_thresholdcatboost.get_roc_curvecatboost.get_scale_and_biascatboost.get_test_evalcatboost.get_test_evalscatboost.get_tree_leaf_countscatboost.grid_searchcatboost.iterate_leaf_indexescatboost.load_modelcatboost.load_poolcatboost.model_based_evalcatboost.normalize_model_from_poolcatboost.plot_partial_dependencecatboost.plot_predictionscatboost.plot_treecatboost.pool.get_baselinecatboost.pool.get_cat_feature_indicescatboost.pool.get_embedding_feature_indicescatboost.pool.get_feature_namescatboost.pool.get_featurescatboost.pool.get_group_id_hashcatboost.pool.get_labelcatboost.pool.get_text_feature_indicescatboost.pool.get_weightcatboost.pool.has_labelcatboost.pool.is_emptycatboost.pool.is_quantizedcatboost.pool.num_colcatboost.pool.num_pairscatboost.pool.num_rowcatboost.pool.quantizecatboost.pool.savecatboost.pool.save_quantization_borderscatboost.pool.set_baselinecatboost.pool.set_feature_namescatboost.pool.set_group_idcatboost.pool.set_group_weightcatboost.pool.set_pairscatboost.pool.set_pairs_weightcatboost.pool.set_subgroup_idcatboost.pool.set_timestampcatboost.pool.set_weightcatboost.pool.shapecatboost.pool.slicecatboost.pool.train_eval_splitcatboost.predictcatboost.predict_log_probacatboost.randomized_searchcatboost.restore_handlecatboost.run_workercatboost.sample_gaussian_processcatboost.save_borderscatboost.save_modelcatboost.save_poolcatboost.select_featurescatboost.set_leaf_valuescatboost.set_metadatacatboost.set_probability_thresholdcatboost.set_scale_and_biascatboost.shrinkcatboost.staged_predictcatboost.staged_predict_log_probacatboost.sum_modelscatboost.Tokenizercatboost.tokenizer.tokenizecatboost.traincatboost.virtual_ensembles_predict

Dependencies:jsonlite

Readme and manuals

Help Manual

Help pageTopics
Calculate feature statistics.catboost.calc_feature_statistics
Calculate the leaf index each object falls into, per treecatboost.calc_leaf_indexes
Support caret interfacecatboost.caret
Compare metrics of two models.catboost.compare
Resolve training options without training a modelcatboost.compute_training_options
Create a batch metric calcercatboost.create_metric_calcer
Cross-validate model.catboost.cv
Calculate dataset statisticscatboost.dataset_statistics
Create text Dictionarycatboost.Dictionary
Apply a Dictionarycatboost.dictionary.apply
Identifier of the end-of-sentence tokencatboost.dictionary.end_of_sentence_token_id
Train a Dictionarycatboost.dictionary.fit
Get a single token by idcatboost.dictionary.get_token
Get tokens by idcatboost.dictionary.get_tokens
Most frequent tokenscatboost.dictionary.get_top_tokens
Load a Dictionarycatboost.dictionary.load
Smallest unused token identifiercatboost.dictionary.min_unused_token_id
Save a Dictionarycatboost.dictionary.save
Dictionary sizecatboost.dictionary.size
Identifier of the unknown-token placeholdercatboost.dictionary.unknown_token_id
Drop unused features information from modelcatboost.drop_unused_features
Evaluate the impact of feature sets.catboost.eval_feature
Evaluate a metric directly on raw label/approx arrayscatboost.eval_metric
Calculate metrics.catboost.eval_metrics
Number of categorical features in a FeaturesDatacatboost.features_data.get_cat_feature_count
Total number of features in a FeaturesDatacatboost.features_data.get_feature_count
Get feature names from a FeaturesDatacatboost.features_data.get_feature_names
Number of numerical features in a FeaturesDatacatboost.features_data.get_num_feature_count
Number of objects in a FeaturesDatacatboost.features_data.get_object_count
Create a FeaturesData containercatboost.FeaturesData
Get best iterationcatboost.get_best_iteration
Get best scorecatboost.get_best_score
Get a model's float feature borderscatboost.get_borders
Get evaluation resultscatboost.get_evals_result
Calculate the feature importancescatboost.get_feature_importance
Get the model's tree leaf valuescatboost.get_leaf_values
Get the model's tree leaf weightscatboost.get_leaf_weights
Get model metadatacatboost.get_metadata
Get the feature names used by a modelcatboost.get_model_feature_names
Model parameterscatboost.get_model_params
Calculate the object importancescatboost.get_object_importance
Plain Model parameterscatboost.get_plain_params
Get the binary-classification probability thresholdcatboost.get_probability_threshold
Build a ROC curvecatboost.get_roc_curve
Get model scale and biascatboost.get_scale_and_bias
Get the model's raw predictions on its eval setcatboost.get_test_eval
Get the model's raw predictions on all its eval setscatboost.get_test_evals
Get the number of leaves in each tree of the modelcatboost.get_tree_leaf_counts
Exhaustive search over specified parameter values.catboost.grid_search
Iterate the leaf index each object falls into, per treecatboost.iterate_leaf_indexes
Load the modelcatboost.load_model
Create a datasetcatboost.load_pool
Model-based feature evaluation.catboost.model_based_eval
Rescale a model so its raw predictions on a pool span [0, 1]catboost.normalize_model_from_pool
Compute the partial dependence of the model on one or two featurescatboost.plot_partial_dependence
Vary one feature's value and observe the predictioncatboost.plot_predictions
Plot a single tree's structure.catboost.plot_tree
Get baseline from a Poolcatboost.pool.get_baseline
Get categorical feature indices from a Poolcatboost.pool.get_cat_feature_indices
Get embedding feature indices from a Poolcatboost.pool.get_embedding_feature_indices
Get feature names from a Poolcatboost.pool.get_feature_names
Get the feature matrix from a Poolcatboost.pool.get_features
Get group id hashes from a Poolcatboost.pool.get_group_id_hash
Get labels from a Poolcatboost.pool.get_label
Get text feature indices from a Poolcatboost.pool.get_text_feature_indices
Get weights from a Poolcatboost.pool.get_weight
Has the Pool got label datacatboost.pool.has_label
Is the Pool emptycatboost.pool.is_empty
Is the Pool quantizedcatboost.pool.is_quantized
Number of columns in a Poolcatboost.pool.num_col
Number of pairs in a Poolcatboost.pool.num_pairs
Number of rows in a Poolcatboost.pool.num_row
Quantize a Poolcatboost.pool.quantize
Save a quantized Pool to CatBoost's binary quantized-pool formatcatboost.pool.save
Save a Pool's quantization borders to a filecatboost.pool.save_quantization_borders
Set baseline on a Poolcatboost.pool.set_baseline
Set feature names on a Poolcatboost.pool.set_feature_names
Set group ids on a Poolcatboost.pool.set_group_id
Set group weights on a Poolcatboost.pool.set_group_weight
Set pairs on a Poolcatboost.pool.set_pairs
Set pair weights on a Poolcatboost.pool.set_pairs_weight
Set subgroup ids on a Poolcatboost.pool.set_subgroup_id
Set timestamps on a Poolcatboost.pool.set_timestamp
Set weights on a Poolcatboost.pool.set_weight
Shape of a Poolcatboost.pool.shape
Slice a Poolcatboost.pool.slice
Split a Pool into train/eval subsetscatboost.pool.train_eval_split
Get predictions from a CatBoost modelcatboost.predict predict.catboost.Model
Predict class log-probabilitiescatboost.predict_log_proba
Randomized search on hyper parameters.catboost.randomized_search
Restore or complete model handle after de-serializingcatboost.restore_handle
Run a distributed-training worker.catboost.run_worker
Sample from a Gaussian Process posteriorcatboost.sample_gaussian_process
Save a model's float feature borders to a filecatboost.save_borders
Save the modelcatboost.save_model
Save the datasetcatboost.save_pool
Select the best features by recursive elimination.catboost.select_features
Set the model's tree leaf valuescatboost.set_leaf_values
Set model metadatacatboost.set_metadata
Set the binary-classification probability thresholdcatboost.set_probability_threshold
Set model scale and biascatboost.set_scale_and_bias
Shrink the modelcatboost.shrink
Apply the model for each treecatboost.staged_predict
Predict class log-probabilities at each stagecatboost.staged_predict_log_proba
Sum models.catboost.sum_models
Create text Tokenizercatboost.Tokenizer
Tokenize a stringcatboost.tokenizer.tokenize
Train the modelcatboost.train
Apply the model with several virtual ensemblescatboost.virtual_ensembles_predict
Dimensions of catboost.Pooldim.catboost.Pool
Dimension names of catboost.Pooldimnames.catboost.Pool
Head of catboost.Poolhead.catboost.Pool
Print basic information about modelprint.catboost.Model
Print catboost.Poolprint.catboost.Pool
Print basic information about modelsummary.catboost.Model
Tail of catboost.Pooltail.catboost.Pool