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mikropml  

User-Friendly R Package for Supervised Machine Learning Pipelines
View on CRAN: Click here


Download and install mikropml package within the R console
Install from CRAN:
install.packages("mikropml")

Install from Github:
library("remotes")
install_github("cran/mikropml")

Install by package version:
library("remotes")
install_version("mikropml", "1.6.1")



Attach the package and use:
library("mikropml")
Maintained by
Kelly Sovacool
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-11-23
Latest Update: 2023-08-21
Description:
An interface to build machine learning models for classification and regression problems. 'mikropml' implements the ML pipeline described by Top
How to cite:
Kelly Sovacool (2020). mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines. R package version 1.6.1, https://cran.r-project.org/web/packages/mikropml
Previous versions and publish date:
0.0.1 (2020-11-23 10:40), 0.0.2 (2020-12-03 09:10), 1.0.0 (2021-05-14 01:20), 1.1.0 (2021-08-10 17:40), 1.1.1 (2021-09-14 08:50), 1.2.0 (2021-11-11 00:10), 1.2.1 (2022-01-30 23:30), 1.2.2 (2022-02-03 23:50), 1.3.0 (2022-05-20 17:40), 1.4.0 (2022-10-16 09:30), 1.5.0 (2023-01-16 20:00), 1.6.0 (2023-04-15 01:00)
Other packages that cited mikropml R package
View mikropml citation profile
Other R packages that mikropml depends, imports, suggests or enhances
Functions, R codes and Examples using the mikropml R package
Some associated functions: abort_packages_not_installed . bootstrap_performance . bounds . calc_balanced_precision . calc_baseline_precision . calc_mean_perf . calc_perf_bootstrap_split . calc_perf_metrics . calc_pvalue . change_to_num . check_all . check_cat_feats . check_corr_thresh . check_dataset . check_features . check_group_partitions . check_groups . check_kfold . check_method . check_ntree . check_outcome_column . check_outcome_value . check_packages_installed . check_perf_metric_function . check_perf_metric_name . check_permute . check_remove_var . check_seed . check_training_frac . check_training_indices . cluster_corr_mat . collapse_correlated_features . combine_hp_performance . compare_models . create_grouped_data_partition . create_grouped_k_multifolds . define_cv . find_permuted_perf_metric . flatten_corr_mat . get_binary_corr_mat . get_caret_dummyvars_df . get_caret_processed_df . get_corr_feats . get_difference . get_feature_importance . get_groups_from_clusters . get_hp_performance . get_hyperparams_from_df . get_hyperparams_list . get_outcome_type . get_partition_indices . get_perf_metric_fn . get_perf_metric_name . get_performance_tbl . get_seeds_trainControl . get_tuning_grid . group_correlated_features . is_whole_number . keep_groups_in_cv_partitions . mikropml-package . mutate_all_types . otu_data_preproc . otu_mini_bin . otu_mini_bin_results_glmnet . otu_mini_bin_results_rf . otu_mini_bin_results_rpart2 . otu_mini_bin_results_svmRadial . otu_mini_bin_results_xgbTree . otu_mini_cont_results_glmnet . otu_mini_cont_results_nocv . otu_mini_cv . otu_mini_multi . otu_mini_multi_group . otu_mini_multi_results_glmnet . otu_small . pbtick . permute_p_value . plot_curves . plot_hp_performance . plot_model_performance . preprocess_data . process_cat_feats . process_cont_feats . process_novar_feats . radix_sort . randomize_feature_order . reexports . remove_singleton_columns . replace_spaces . rm_missing_outcome . run_ml . select_apply . sensspec . set_hparams_glmnet . set_hparams_rf . set_hparams_rpart2 . set_hparams_svmRadial . set_hparams_xgbTree . shared_ggprotos . shuffle_group . split_outcome_features . tidy_perf_data . train_model . 
Some associated R codes: checks.R . compare_models.R . corr_feats.R . cross_val.R . data.R . feature_importance.R . hyperparameters.R . mikropml.R . partition.R . performance.R . plot.R . preprocess.R . reexports.R . run_ml.R . train.R . utils.R .  Full mikropml package functions and examples
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