A Unified Tidy Interface to R's Machine Learning Ecosystem

Documentation for package ‘tidylearn’ version 0.1.0

Help Pages

-- A --

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

-- C --

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

-- E --

explore_dbscan_params

Explore DBSCAN Parameters

-- F --

filter_rules_by_item	Filter Rules by Item
find_related_items	Find Related Items

-- G --

get_pca_loadings	Get PCA Loadings in Wide Format
get_pca_variance	Get Variance Explained Summary

-- I --

inspect_rules

Inspect Association Rules

-- O --

optimal_clusters	Find Optimal Number of Clusters
optimal_hclust_k	Determine Optimal Number of Clusters for Hierarchical Clustering

-- P --

plot.tidylearn_eda	Plot EDA results
plot.tidylearn_model	Plot method for tidylearn models
plot_clusters	Plot Clusters in 2D Space
plot_cluster_comparison	Create Cluster Comparison Plot
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_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.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_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.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

-- R --

recommend_products

Generate Product Recommendations

-- S --

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

-- T --

tidylearn-classification	Classification Functions for tidylearn
tidylearn-core	tidylearn: A Unified Tidy Interface to R's Machine Learning Ecosystem
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_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
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_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_explore	Exploratory Data Analysis Workflow
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_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_gain	Plot gain chart for a classification model
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_regularization_cv	Plot cross-validation results for a regularized regression model
tl_plot_regularization_path	Plot regularization path for a regularized regression 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_pipeline	Make predictions using a pipeline
tl_prepare_data	Data Preprocessing for tidylearn
tl_reduce_dimensions	Integration Functions: Combining Supervised and Unsupervised Learning
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 cross-validation
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

-- V --

visualize_rules

Visualize Association Rules