Integration Workflows: Combining Supervised and Unsupervised Learning

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

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5
)

library(tidylearn)
library(dplyr)
library(ggplot2)

Introduction

This vignette showcases tidylearn's capability to seamlessly integrate supervised and unsupervised learning in multi-step workflows.

Important: These integration functions orchestrate the wrapped packages (stats, glmnet, randomForest, cluster, etc.) rather than implementing new algorithms. tidylearn provides the workflow coordination, while the underlying packages do the computational work. Access raw model objects via model$fit.

Dimensionality Reduction as Preprocessing

Use PCA or MDS to reduce feature space before supervised learning.

Basic Usage

# Reduce dimensions before classification
reduced <- tl_reduce_dimensions(iris,
                                response = "Species",
                                method = "pca",
                                n_components = 3)

# Inspect reduced data
head(reduced$data)

# Train classifier on reduced features (remove .obs_id column first)
reduced_data <- reduced$data %>% select(-starts_with(".obs"))
model_reduced <- tl_model(reduced_data, Species ~ ., method = "logistic")
print(model_reduced)

# Make predictions
preds <- predict(model_reduced)
accuracy <- mean(preds$.pred == iris$Species)
cat("Accuracy with PCA features:", round(accuracy * 100, 1), "%\n")

Comparison: Original vs Reduced Features

# Split data for fair comparison
split <- tl_split(iris, prop = 0.7, stratify = "Species", seed = 123)

# Model with original features
model_original <- tl_model(split$train, Species ~ ., method = "logistic")
preds_original <- predict(model_original, new_data = split$test)
acc_original <- mean(preds_original$.pred == split$test$Species)

# Model with PCA features
reduced_train <- tl_reduce_dimensions(split$train,
                                     response = "Species",
                                     method = "pca",
                                     n_components = 3)

# Remove .obs_id column before modeling (it's just an identifier)
reduced_train_data <- reduced_train$data %>% select(-starts_with(".obs"))
model_pca <- tl_model(reduced_train_data, Species ~ ., method = "logistic")

# Need to transform test data using same PCA
test_predictors <- split$test %>% select(-Species)
test_transformed <- predict(reduced_train$reduction_model, new_data = test_predictors)
test_transformed$Species <- split$test$Species
test_transformed <- test_transformed %>% select(-starts_with(".obs"))

preds_pca <- predict(model_pca, new_data = test_transformed)
acc_pca <- mean(preds_pca$.pred == split$test$Species)

# Compare results
cat("Original features (4):", round(acc_original * 100, 1), "%\n")
cat("PCA features (3):", round(acc_pca * 100, 1), "%\n")
cat("Feature reduction:", round((1 - 3/4) * 100, 1), "%\n")

Cluster-Based Feature Engineering

Add cluster assignments as features to boost supervised learning performance.

Adding Cluster Features

# Add cluster features
data_clustered <- tl_add_cluster_features(iris,
                                         response = "Species",
                                         method = "kmeans",
                                         k = 3)

# Check new features
names(data_clustered)

# Train model with cluster features
model_cluster <- tl_model(data_clustered, Species ~ ., method = "forest")
print(model_cluster)

Performance Comparison

# Compare models with and without cluster features
split_comp <- tl_split(iris, prop = 0.7, stratify = "Species", seed = 42)

# Without cluster features
model_no_cluster <- tl_model(split_comp$train, Species ~ ., method = "forest")
preds_no_cluster <- predict(model_no_cluster, new_data = split_comp$test)
acc_no_cluster <- mean(preds_no_cluster$.pred == split_comp$test$Species)

# With cluster features
train_clustered <- tl_add_cluster_features(split_comp$train,
                                          response = "Species",
                                          method = "kmeans",
                                          k = 3)
model_with_cluster <- tl_model(train_clustered, Species ~ ., method = "forest")

# Need to get cluster model for test data
cluster_model <- attr(train_clustered, "cluster_model")
test_clusters <- predict(cluster_model, new_data = split_comp$test[, -5])
test_clustered <- split_comp$test
test_clustered$cluster_kmeans <- as.factor(test_clusters$cluster)

preds_with_cluster <- predict(model_with_cluster, new_data = test_clustered)
acc_with_cluster <- mean(preds_with_cluster$.pred == split_comp$test$Species)

cat("Without cluster features:", round(acc_no_cluster * 100, 1), "%\n")
cat("With cluster features:", round(acc_with_cluster * 100, 1), "%\n")

Semi-Supervised Learning

Train models with limited labels using cluster-based label propagation.

Training with Limited Labels

# Use only 10% of labels
set.seed(123)
labeled_indices <- sample(nrow(iris), size = 15)  # Only 15 out of 150 labeled!

# Train semi-supervised model
model_semi <- tl_semisupervised(iris, Species ~ .,
                               labeled_indices = labeled_indices,
                               cluster_method = "kmeans",
                               supervised_method = "logistic")

print(model_semi)

# Check how labels were propagated
label_mapping <- model_semi$semisupervised_info$label_mapping
print(label_mapping)

# Evaluate performance
preds_semi <- predict(model_semi)
accuracy_semi <- mean(preds_semi$.pred == iris$Species)

cat("Accuracy with only", length(labeled_indices), "labels:",
    round(accuracy_semi * 100, 1), "%\n")
cat("Proportion of data labeled:", round(length(labeled_indices)/nrow(iris) * 100, 1), "%\n")

Comparison: Semi-Supervised vs Fully Supervised

# Fully supervised with same amount of data
labeled_data <- iris[labeled_indices, ]
model_full <- tl_model(labeled_data, Species ~ ., method = "logistic")
preds_full <- predict(model_full, new_data = iris)
accuracy_full <- mean(preds_full$.pred == iris$Species)

cat("Fully supervised (15 samples):", round(accuracy_full * 100, 1), "%\n")
cat("Semi-supervised (15 labels + propagation):", round(accuracy_semi * 100, 1), "%\n")

Anomaly-Aware Modeling

Detect and handle outliers before supervised learning.

Flagging Anomalies

# Flag anomalies as a feature
model_anomaly_flag <- tl_anomaly_aware(iris, Species ~ .,
                                      response = "Species",
                                      anomaly_method = "dbscan",
                                      action = "flag",
                                      supervised_method = "logistic")

# Check anomaly info
cat("Anomalies detected:", model_anomaly_flag$anomaly_info$n_anomalies, "\n")

Removing Anomalies

# Remove anomalies before training
model_anomaly_remove <- tl_anomaly_aware(iris, Species ~ .,
                                        response = "Species",
                                        anomaly_method = "dbscan",
                                        action = "remove",
                                        supervised_method = "logistic")

cat("Anomalies removed:", model_anomaly_remove$anomalies_removed, "\n")

Stratified Models

Create cluster-specific models for heterogeneous data.

Training Stratified Models

# Train separate models for different clusters
stratified_models <- tl_stratified_models(mtcars, mpg ~ .,
                                         cluster_method = "kmeans",
                                         k = 3,
                                         supervised_method = "linear")

# Check structure
names(stratified_models)
length(stratified_models$supervised_models)

# Predictions using stratified models
preds_stratified <- predict(stratified_models)
head(preds_stratified)

# Calculate RMSE
rmse_stratified <- sqrt(mean((preds_stratified$.pred - mtcars$mpg)^2))
cat("Stratified Model RMSE:", round(rmse_stratified, 2), "\n")

# Compare with single model
model_single <- tl_model(mtcars, mpg ~ ., method = "linear")
preds_single <- predict(model_single)
rmse_single <- sqrt(mean((preds_single$.pred - mtcars$mpg)^2))
cat("Single Model RMSE:", round(rmse_single, 2), "\n")

Complete Integration Workflow

Combining multiple integration techniques:

# Step 1: Split data
workflow_split <- tl_split(iris, prop = 0.7, stratify = "Species", seed = 42)

# Step 2: Reduce dimensions
workflow_reduced <- tl_reduce_dimensions(workflow_split$train,
                                        response = "Species",
                                        method = "pca",
                                        n_components = 3)

# Step 3: Add cluster features to reduced data (remove .obs_id first)
workflow_reduced_clean <- workflow_reduced$data %>% select(-starts_with(".obs"))
workflow_clustered <- tl_add_cluster_features(workflow_reduced_clean,
                                             response = "Species",
                                             method = "kmeans",
                                             k = 3)

# Step 4: Train final model
workflow_model <- tl_model(workflow_clustered, Species ~ ., method = "forest")

print(workflow_model)

# Transform test data through same pipeline
# 1. Apply PCA transformation
test_pca <- predict(workflow_reduced$reduction_model,
                   new_data = workflow_split$test[, -5])
test_pca$Species <- workflow_split$test$Species

# 2. Get cluster assignments
cluster_model_wf <- attr(workflow_clustered, "cluster_model")
test_clusters_wf <- predict(cluster_model_wf,
                            new_data = test_pca[, grep("PC", names(test_pca))])
test_pca$cluster_kmeans <- as.factor(test_clusters_wf$cluster)

# 3. Predict
workflow_preds <- predict(workflow_model, new_data = test_pca)
workflow_accuracy <- mean(workflow_preds$.pred == workflow_split$test$Species)

cat("Complete Workflow Accuracy:", round(workflow_accuracy * 100, 1), "%\n")

Practical Example: Credit Risk Assessment

# Simulate credit data
set.seed(42)
n <- 500
credit_data <- data.frame(
  age = rnorm(n, 40, 12),
  income = rnorm(n, 50000, 20000),
  debt_ratio = runif(n, 0, 0.5),
  credit_score = rnorm(n, 700, 100),
  years_employed = rpois(n, 5)
)

# Create target variable (default risk)
credit_data$default <- factor(
  ifelse(credit_data$debt_ratio > 0.4 & credit_data$credit_score < 650, "Yes", "No")
)

# Split data
credit_split <- tl_split(credit_data, prop = 0.7, stratify = "default", seed = 123)

# Strategy 1: Add customer segments as features
credit_clustered <- tl_add_cluster_features(credit_split$train,
                                           response = "default",
                                           method = "kmeans",
                                           k = 4)

model_credit <- tl_model(credit_clustered, default ~ ., method = "forest")

# Transform test data
cluster_model_credit <- attr(credit_clustered, "cluster_model")
test_clusters_credit <- predict(cluster_model_credit,
                               new_data = credit_split$test[, -6])
test_credit <- credit_split$test
test_credit$cluster_kmeans <- as.factor(test_clusters_credit$cluster)

preds_credit <- predict(model_credit, new_data = test_credit)
accuracy_credit <- mean(preds_credit$.pred == credit_split$test$default)

cat("Credit Risk Model Accuracy:", round(accuracy_credit * 100, 1), "%\n")

Key Advantages of Integration

1. Better Performance: Combining techniques often outperforms individual approaches
2. Feature Engineering: Unsupervised methods create informative features
3. Data Efficiency: Semi-supervised learning uses unlabeled data effectively
4. Robustness: Anomaly detection improves model reliability
5. Interpretability: Cluster-specific models are easier to understand

Best Practices

1. Always transform test data using the same unsupervised models as training data
2. Experiment with different combinations to find the best approach
3. Use semi-supervised learning when labels are expensive to obtain
4. Consider stratified models for heterogeneous datasets
5. Validate performance on held-out test data

Summary

tidylearn's integration functions provide powerful ways to combine supervised and unsupervised learning:

• tl_reduce_dimensions(): Use PCA/MDS as preprocessing
• tl_add_cluster_features(): Engineer features from clusters
• tl_semisupervised(): Train with limited labels
• tl_anomaly_aware(): Handle outliers intelligently
• tl_stratified_models(): Create cluster-specific models

These functions orchestrate the underlying packages (stats, cluster, glmnet, randomForest, etc.) to enable multi-step workflows with a consistent interface.

# Final integrated example
final_data <- iris
final_split <- tl_split(final_data, prop = 0.7, stratify = "Species", seed = 999)

# Combine PCA + clustering
final_reduced <- tl_reduce_dimensions(final_split$train,
                                     response = "Species",
                                     method = "pca",
                                     n_components = 3)
# Remove .obs_id column before clustering
final_reduced_clean <- final_reduced$data %>% select(-starts_with(".obs"))
final_clustered <- tl_add_cluster_features(final_reduced_clean,
                                          response = "Species",
                                          method = "kmeans",
                                          k = 3)
final_model <- tl_model(final_clustered, Species ~ ., method = "logistic")

cat("Final integrated model created successfully!\n")
print(final_model)

tidylearn documentation built on Feb. 6, 2026, 5:07 p.m.