Man pages for tidylearn
A Unified Tidy Interface to R's Machine Learning Ecosystem
| augment_dbscan | Augment Data with DBSCAN Cluster Assignments |
| augment_hclust | Augment Data with Hierarchical Cluster Assignments |
| augment_kmeans | Augment Data with K-Means Cluster Assignments |
| augment_pam | Augment Data with PAM Cluster Assignments |
| augment_pca | Augment Original Data with PCA Scores |
| calc_validation_metrics | Calculate Cluster Validation Metrics |
| calc_wss | Calculate Within-Cluster Sum of Squares for Different k |
| compare_clusterings | Compare Multiple Clustering Results |
| compare_distances | Compare Distance Methods |
| create_cluster_dashboard | Create Summary Dashboard |
| explore_dbscan_params | Explore DBSCAN Parameters |
| filter_rules_by_item | Filter Rules by Item |
| find_related_items | Find Related Items |
| get_pca_loadings | Get PCA Loadings in Wide Format |
| get_pca_variance | Get Variance Explained Summary |
| inspect_rules | Inspect Association Rules |
| optimal_clusters | Find Optimal Number of Clusters |
| optimal_hclust_k | Determine Optimal Number of Clusters for Hierarchical... |
| pipe | Pipe operator |
| plot_cluster_comparison | Create Cluster Comparison Plot |
| plot_clusters | Plot Clusters in 2D Space |
| plot_cluster_sizes | Plot Cluster Size Distribution |
| plot_dendrogram | Plot Dendrogram with Cluster Highlights |
| plot_distance_heatmap | Create Distance Heatmap |
| plot_elbow | Create Elbow Plot for K-Means |
| plot_gap_stat | Plot Gap Statistic |
| plot_knn_dist | Plot k-NN Distance Plot |
| plot_mds | Plot MDS Configuration |
| plot_silhouette | Plot Silhouette Analysis |
| plot.tidylearn_eda | Plot EDA results |
| plot.tidylearn_model | Plot method for tidylearn models |
| plot_variance_explained | Plot Variance Explained (PCA) |
| predict.tidylearn_model | Predict using a tidylearn model |
| predict.tidylearn_stratified | Predict from stratified models |
| predict.tidylearn_transfer | Predict with transfer learning model |
| print.tidy_apriori | Print Method for tidy_apriori |
| print.tidy_dbscan | Print Method for tidy_dbscan |
| print.tidy_gap | Print Method for tidy_gap |
| print.tidy_hclust | Print Method for tidy_hclust |
| print.tidy_kmeans | Print Method for tidy_kmeans |
| print.tidylearn_automl | Print auto ML results |
| print.tidylearn_eda | Print EDA results |
| print.tidylearn_model | Print method for tidylearn models |
| print.tidylearn_pipeline | Print a tidylearn pipeline |
| print.tidy_mds | Print Method for tidy_mds |
| print.tidy_pam | Print Method for tidy_pam |
| print.tidy_pca | Print Method for tidy_pca |
| print.tidy_silhouette | Print Method for tidy_silhouette |
| recommend_products | Generate Product Recommendations |
| standardize_data | Standardize Data |
| suggest_eps | Suggest eps Parameter for DBSCAN |
| summarize_rules | Summarize Association Rules |
| summary.tidylearn_model | Summary method for tidylearn models |
| summary.tidylearn_pipeline | Summarize a tidylearn pipeline |
| tidy_apriori | Tidy Apriori Algorithm |
| tidy_clara | Tidy CLARA (Clustering Large Applications) |
| tidy_cutree | Cut Hierarchical Clustering Tree |
| tidy_dbscan | Tidy DBSCAN Clustering |
| tidy_dendrogram | Plot Dendrogram |
| tidy_dist | Tidy Distance Matrix Computation |
| tidy_gap_stat | Tidy Gap Statistic |
| tidy_gower | Gower Distance Calculation |
| tidy_hclust | Tidy Hierarchical Clustering |
| tidy_kmeans | Tidy K-Means Clustering |
| tidy_knn_dist | Compute k-NN Distances |
| tidylearn-classification | Classification Functions for tidylearn |
| tidylearn-core | tidylearn: A Unified Tidy Interface to R's Machine Learning... |
| tidylearn-deep-learning | Deep Learning for tidylearn |
| tidylearn-diagnostics | Advanced Diagnostics Functions for tidylearn |
| tidylearn-interactions | Interaction Analysis Functions for tidylearn |
| tidylearn-metrics | Metrics Functionality for tidylearn |
| tidylearn-model-selection | Model Selection Functions for tidylearn |
| tidylearn-neural-networks | Neural Networks for tidylearn |
| tidylearn-pipeline | Model Pipeline Functions for tidylearn |
| tidylearn-regression | Regression Functions for tidylearn |
| tidylearn-regularization | Regularization Functions for tidylearn |
| tidylearn-svm | Support Vector Machines for tidylearn |
| tidylearn-trees | Tree-based Methods for tidylearn |
| tidylearn-tuning | Hyperparameter Tuning Functions for tidylearn |
| tidylearn-visualization | Visualization Functions for tidylearn |
| tidylearn-xgboost | XGBoost Functions for tidylearn |
| tidy_mds | Tidy Multidimensional Scaling |
| tidy_mds_classical | Classical (Metric) MDS |
| tidy_mds_kruskal | Kruskal's Non-metric MDS |
| tidy_mds_sammon | Sammon Mapping |
| tidy_mds_smacof | SMACOF MDS (Metric or Non-metric) |
| tidy_pam | Tidy PAM (Partitioning Around Medoids) |
| tidy_pca | Tidy Principal Component Analysis |
| tidy_pca_biplot | Create PCA Biplot |
| tidy_pca_screeplot | Create PCA Scree Plot |
| tidy_rules | Convert Association Rules to Tidy Tibble |
| tidy_silhouette | Tidy Silhouette Analysis |
| tidy_silhouette_analysis | Silhouette Analysis Across Multiple k Values |
| tl_add_cluster_features | Cluster-Based Features |
| tl_anomaly_aware | Anomaly-Aware Supervised Learning |
| tl_auto_interactions | Find important interactions automatically |
| tl_auto_ml | High-Level Workflows for Common Machine Learning Patterns |
| tl_calc_classification_metrics | Calculate classification metrics |
| tl_calculate_pr_auc | Calculate the area under the precision-recall curve |
| tl_check_assumptions | Check model assumptions |
| tl_compare_cv | Compare models using cross-validation |
| tl_compare_pipeline_models | Compare models from a pipeline |
| tl_cv | Cross-validation for tidylearn models |
| tl_dashboard | Create interactive visualization dashboard for a model |
| tl_default_param_grid | Create pre-defined parameter grids for common models |
| tl_detect_outliers | Detect outliers in the data |
| tl_diagnostic_dashboard | Create a comprehensive diagnostic dashboard |
| tl_evaluate | Evaluate a tidylearn model |
| tl_evaluate_thresholds | Evaluate metrics at different thresholds |
| tl_explore | Exploratory Data Analysis Workflow |
| tl_extract_importance | Extract importance from a tree-based model |
| tl_extract_importance_regularized | Extract importance from a regularized regression model |
| tl_fit_boost | Fit a gradient boosting model |
| tl_fit_deep | Fit a deep learning model |
| tl_fit_elastic_net | Fit an Elastic Net regression model |
| tl_fit_forest | Fit a random forest model |
| tl_fit_lasso | Fit a Lasso regression model |
| tl_fit_linear | Fit a linear regression model |
| tl_fit_logistic | Fit a logistic regression model |
| tl_fit_nn | Fit a neural network model |
| tl_fit_polynomial | Fit a polynomial regression model |
| tl_fit_regularized | Fit a regularized regression model (Ridge, Lasso, or Elastic... |
| tl_fit_ridge | Fit a Ridge regression model |
| tl_fit_svm | Fit a support vector machine model |
| tl_fit_tree | Fit a decision tree model |
| tl_fit_xgboost | Fit an XGBoost model |
| tl_get_best_model | Get the best model from a pipeline |
| tl_influence_measures | Calculate influence measures for a linear model |
| tl_interaction_effects | Calculate partial effects based on a model with interactions |
| tl_load_pipeline | Load a pipeline from disk |
| tl_model | Create a tidylearn model |
| tl_pipeline | Create a modeling pipeline |
| tl_plot_actual_predicted | Plot actual vs predicted values for a regression model |
| tl_plot_calibration | Plot calibration curve for a classification model |
| tl_plot_confusion | Plot confusion matrix for a classification model |
| tl_plot_cv_comparison | Plot comparison of cross-validation results |
| tl_plot_cv_results | Plot cross-validation results |
| tl_plot_deep_architecture | Plot deep learning model architecture |
| tl_plot_deep_history | Plot deep learning model training history |
| tl_plot_diagnostics | Plot diagnostics for a regression model |
| tl_plot_gain | Plot gain chart for a classification model |
| tl_plot_importance | Plot variable importance for tree-based models |
| tl_plot_importance_comparison | Plot feature importance across multiple models |
| tl_plot_importance_regularized | Plot variable importance for a regularized regression model |
| tl_plot_influence | Plot influence diagnostics |
| tl_plot_interaction | Plot interaction effects |
| tl_plot_intervals | Create confidence and prediction interval plots |
| tl_plot_lift | Plot lift chart for a classification model |
| tl_plot_model_comparison | Plot model comparison |
| tl_plot_nn_architecture | Plot neural network architecture |
| tl_plot_nn_tuning | Plot neural network training history |
| tl_plot_partial_dependence | Plot partial dependence for tree-based models |
| tl_plot_precision_recall | Plot precision-recall curve for a classification model |
| tl_plot_regularization_cv | Plot cross-validation results for a regularized regression... |
| tl_plot_regularization_path | Plot regularization path for a regularized regression model |
| tl_plot_residuals | Plot residuals for a regression model |
| tl_plot_roc | Plot ROC curve for a classification model |
| tl_plot_svm_boundary | Plot SVM decision boundary |
| tl_plot_svm_tuning | Plot SVM tuning results |
| tl_plot_tree | Plot a decision tree |
| tl_plot_tuning_results | Plot hyperparameter tuning results |
| tl_plot_xgboost_importance | Plot feature importance for an XGBoost model |
| tl_plot_xgboost_shap_dependence | Plot SHAP dependence for a specific feature |
| tl_plot_xgboost_shap_summary | Plot SHAP summary for XGBoost model |
| tl_plot_xgboost_tree | Plot XGBoost tree visualization |
| tl_predict_boost | Predict using a gradient boosting model |
| tl_predict_deep | Predict using a deep learning model |
| tl_predict_elastic_net | Predict using an Elastic Net regression model |
| tl_predict_forest | Predict using a random forest model |
| tl_predict_lasso | Predict using a Lasso regression model |
| tl_predict_linear | Predict using a linear regression model |
| tl_predict_logistic | Predict using a logistic regression model |
| tl_predict_nn | Predict using a neural network model |
| tl_predict_pipeline | Make predictions using a pipeline |
| tl_predict_polynomial | Predict using a polynomial regression model |
| tl_predict_regularized | Predict using a regularized regression model |
| tl_predict_ridge | Predict using a Ridge regression model |
| tl_predict_svm | Predict using a support vector machine model |
| tl_predict_tree | Predict using a decision tree model |
| tl_predict_xgboost | Predict using an XGBoost model |
| tl_prepare_data | Data Preprocessing for tidylearn |
| tl_reduce_dimensions | Integration Functions: Combining Supervised and Unsupervised... |
| tl_run_pipeline | Run a tidylearn pipeline |
| tl_save_pipeline | Save a pipeline to disk |
| tl_semisupervised | Semi-Supervised Learning via Clustering |
| tl_split | Split data into train and test sets |
| tl_step_selection | Perform stepwise selection on a linear model |
| tl_stratified_models | Stratified Features via Clustering |
| tl_test_interactions | Test for significant interactions between variables |
| tl_test_model_difference | Perform statistical comparison of models using... |
| tl_transfer_learning | Transfer Learning Workflow |
| tl_tune_deep | Tune a deep learning model |
| tl_tune_grid | Tune hyperparameters for a model using grid search |
| tl_tune_nn | Tune a neural network model |
| tl_tune_random | Tune hyperparameters for a model using random search |
| tl_tune_xgboost | Tune XGBoost hyperparameters |
| tl_version | Get tidylearn version information |
| tl_xgboost_shap | Generate SHAP values for XGBoost model interpretation |
| visualize_rules | Visualize Association Rules |
