# A Unified Tidy Interface to R's Machine Learning Ecosystem

## Documentation for package ‘tidylearn’ version 0.3.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

A C E F G I O P R S T V

## -- 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_data | Print a tidylearn_data object
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-read | Data Reading Functions for tidylearn
tidylearn-read-backends | Data Reading Backends for tidylearn
tidylearn-regression | Regression Functions for tidylearn
tidylearn-regularization | Regularization Functions for tidylearn
tidylearn-svm | Support Vector Machines for tidylearn
tidylearn-tables | Table Functions for tidylearn
tidylearn-trees | Tree-based Methods for tidylearn
tidylearn-tuning | Hyperparameter Tuning Functions for tidylearn
tidylearn-visualization | Visualization Functions for tidylearn
tidylearn-workflows | High-Level Workflows for Common Machine Learning Patterns
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 | Auto ML: Automated Machine Learning Workflow
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 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 model
tl_plot_regularization_path | Plot regularization path for a regularized 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_read | Read data from diverse sources
tl_read_bigquery | Read from Google BigQuery
tl_read_csv | Read a CSV file
tl_read_db | Read from a DBI database connection
tl_read_dir | Read all matching files from a directory
tl_read_excel | Read an Excel file
tl_read_github | Read from GitHub
tl_read_json | Read a JSON file
tl_read_kaggle | Read from Kaggle
tl_read_mysql | Read from a MySQL/MariaDB database
tl_read_parquet | Read a Parquet file
tl_read_postgres | Read from a PostgreSQL database
tl_read_rdata | Read an RData file
tl_read_rds | Read an RDS file
tl_read_s3 | Read from Amazon S3
tl_read_sqlite | Read from a SQLite database
tl_read_tsv | Read a TSV file
tl_read_zip | Read data from a zip archive
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_table | Create formatted tables for tidylearn models
tl_table_clusters | Formatted cluster summary table
tl_table_coefficients | Formatted model coefficients table
tl_table_comparison | Compare multiple models in a formatted table
tl_table_confusion | Formatted confusion matrix table
tl_table_importance | Formatted feature importance table
tl_table_loadings | Formatted PCA loadings table
tl_table_metrics | Formatted evaluation metrics table
tl_table_variance | Formatted PCA variance explained table
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 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