tidylearn0.1.0 package

Augment Data with DBSCAN Cluster Assignments

Augment Data with Hierarchical Cluster Assignments

Augment Data with K-Means Cluster Assignments

Augment Data with PAM Cluster Assignments

Augment Original Data with PCA Scores

Calculate Cluster Validation Metrics

Calculate Within-Cluster Sum of Squares for Different k

Compare Multiple Clustering Results

Compare Distance Methods

Create Summary Dashboard

Explore DBSCAN Parameters

Filter Rules by Item

Find Related Items

Get PCA Loadings in Wide Format

Get Variance Explained Summary

Inspect Association Rules

Find Optimal Number of Clusters

Determine Optimal Number of Clusters for Hierarchical Clustering

Pipe operator

Create Cluster Comparison Plot

Plot Cluster Size Distribution

Plot Clusters in 2D Space

Plot Dendrogram with Cluster Highlights

Create Distance Heatmap

Create Elbow Plot for K-Means

Plot Gap Statistic

Plot k-NN Distance Plot

Plot MDS Configuration

Plot Silhouette Analysis

Plot Variance Explained (PCA)

Plot EDA results

Plot method for tidylearn models

Predict using a tidylearn model

Predict from stratified models

Predict with transfer learning model

Print Method for tidy_apriori

Print Method for tidy_dbscan

Print Method for tidy_gap

Print Method for tidy_hclust

Print Method for tidy_kmeans

Print Method for tidy_mds

Print Method for tidy_pam

Print Method for tidy_pca

Print Method for tidy_silhouette

Print auto ML results

Print EDA results

Print method for tidylearn models

Print a tidylearn pipeline

Generate Product Recommendations

Standardize Data

Suggest eps Parameter for DBSCAN

Summarize Association Rules

Summary method for tidylearn models

Summarize a tidylearn pipeline

Tidy Apriori Algorithm

Tidy CLARA (Clustering Large Applications)

Cut Hierarchical Clustering Tree

Tidy DBSCAN Clustering

Plot Dendrogram

Tidy Distance Matrix Computation

Tidy Gap Statistic

Gower Distance Calculation

Tidy Hierarchical Clustering

Tidy K-Means Clustering

Compute k-NN Distances

Classical (Metric) MDS

Kruskal's Non-metric MDS

Sammon Mapping

SMACOF MDS (Metric or Non-metric)

Tidy Multidimensional Scaling

Tidy PAM (Partitioning Around Medoids)

Create PCA Biplot

Create PCA Scree Plot

Tidy Principal Component Analysis

Convert Association Rules to Tidy Tibble

Silhouette Analysis Across Multiple k Values

Tidy Silhouette Analysis

Classification Functions for tidylearn

tidylearn: A Unified Tidy Interface to R's Machine Learning Ecosystem

Deep Learning for tidylearn

Advanced Diagnostics Functions for tidylearn

Interaction Analysis Functions for tidylearn

Metrics Functionality for tidylearn

Model Selection Functions for tidylearn

Neural Networks for tidylearn

Model Pipeline Functions for tidylearn

Regression Functions for tidylearn

Regularization Functions for tidylearn

Support Vector Machines for tidylearn

Tree-based Methods for tidylearn

Hyperparameter Tuning Functions for tidylearn

Visualization Functions for tidylearn

XGBoost Functions for tidylearn

Cluster-Based Features

Anomaly-Aware Supervised Learning

Find important interactions automatically

High-Level Workflows for Common Machine Learning Patterns

Calculate classification metrics

Calculate the area under the precision-recall curve

Check model assumptions

Compare models using cross-validation

Compare models from a pipeline

Cross-validation for tidylearn models

Create interactive visualization dashboard for a model

Create pre-defined parameter grids for common models

Detect outliers in the data

Create a comprehensive diagnostic dashboard

Evaluate metrics at different thresholds

Evaluate a tidylearn model

Exploratory Data Analysis Workflow

Extract importance from a regularized regression model

Extract importance from a tree-based model

Fit a gradient boosting model

Fit a deep learning model

Fit an Elastic Net regression model

Fit a random forest model

Fit a Lasso regression model

Fit a linear regression model

Fit a logistic regression model

Fit a neural network model

Fit a polynomial regression model

Fit a regularized regression model (Ridge, Lasso, or Elastic Net)

Fit a Ridge regression model

Fit a support vector machine model

Fit a decision tree model

Fit an XGBoost model

Get the best model from a pipeline

Calculate influence measures for a linear model

Calculate partial effects based on a model with interactions

Load a pipeline from disk

Create a tidylearn model

Create a modeling pipeline

Plot actual vs predicted values for a regression model

Plot calibration curve for a classification model

Plot confusion matrix for a classification model

Plot comparison of cross-validation results

Plot cross-validation results

Plot deep learning model architecture

Plot deep learning model training history

Plot diagnostics for a regression model

Plot gain chart for a classification model

Plot feature importance across multiple models

Plot variable importance for a regularized regression model

Plot variable importance for tree-based models

Plot influence diagnostics

Plot interaction effects

Create confidence and prediction interval plots

Plot lift chart for a classification model

Plot model comparison

Plot neural network architecture

Plot neural network training history

Plot partial dependence for tree-based models

Plot precision-recall curve for a classification model

Plot cross-validation results for a regularized regression model

Plot regularization path for a regularized regression model

Plot residuals for a regression model

Plot ROC curve for a classification model

Plot SVM decision boundary

Plot SVM tuning results

Plot a decision tree

Plot hyperparameter tuning results

Plot feature importance for an XGBoost model

Plot SHAP dependence for a specific feature

Plot SHAP summary for XGBoost model

Plot XGBoost tree visualization

Predict using a gradient boosting model

Predict using a deep learning model

Predict using an Elastic Net regression model

Predict using a random forest model

Predict using a Lasso regression model

Predict using a linear regression model

Predict using a logistic regression model

Predict using a neural network model

Make predictions using a pipeline

Predict using a polynomial regression model

Predict using a regularized regression model

Predict using a Ridge regression model

Predict using a support vector machine model

Predict using a decision tree model

Predict using an XGBoost model

Data Preprocessing for tidylearn

Integration Functions: Combining Supervised and Unsupervised Learning

Run a tidylearn pipeline

Save a pipeline to disk

Semi-Supervised Learning via Clustering

Split data into train and test sets

Perform stepwise selection on a linear model

Stratified Features via Clustering

Test for significant interactions between variables

Perform statistical comparison of models using cross-validation

Transfer Learning Workflow

Tune a deep learning model

Tune hyperparameters for a model using grid search

Tune a neural network model

Tune hyperparameters for a model using random search

Tune XGBoost hyperparameters

Get tidylearn version information

Generate SHAP values for XGBoost model interpretation

Visualize Association Rules