Classification models are used to predict categorical outcomes, such as whether a customer will buy a product (Yes\/No) or which category an observation belongs to. In R, logistic regression and decision trees are two widely used classification techniques.<\/p>\n\n\n\n
Logistic regression predicts a binary outcome using one or more predictors. The output is a probability between 0 and 1.<\/p>\n\n\n\n
# Load dataset
data <- read.csv(\"customer_purchases.csv\")# Convert target variable to factor
data$HighValue <- factor(ifelse(data$PurchaseAmt > 1000, \"Yes\", \"No\"))# Inspect data
str(data)
table(data$HighValue)<\/pre>\n\n\n\nb) Split Data into Training and Test Sets<\/strong><\/h3>\n\n\n\n
library(caret)set.seed(123)
train_index <- createDataPartition(data$HighValue, p = 0.7, list = FALSE)
train_data <- data[train_index, ]
test_data <- data[-train_index, ]<\/pre>\n\n\n\nc) Fit Logistic Regression Model<\/strong><\/h3>\n\n\n\n
model_logistic <- glm(HighValue ~ Age + Gender + ProductCategory,
data = train_data, family = binomial)summary(model_logistic)<\/pre>\n\n\n\nd) Make Predictions<\/strong><\/h3>\n\n\n\n
# Predict probabilities
probabilities <- predict(model_logistic, test_data, type = \"response\")# Convert probabilities to class labels (threshold = 0.5)
predicted_classes <- ifelse(probabilities > 0.5, \"Yes\", \"No\")
predicted_classes <- factor(predicted_classes, levels = c(\"No\", \"Yes\"))<\/pre>\n\n\n\ne) Evaluate Model Performance<\/strong><\/h3>\n\n\n\n
confusionMatrix(predicted_classes, test_data$HighValue)<\/pre>\n\n\n\n2. Decision Trees<\/strong><\/h1>\n\n\n\n
Decision trees split the data into branches to classify observations based on feature values.<\/p>\n\n\n\n
a) Install and Load Package<\/strong><\/h3>\n\n\n\n
install.packages(\"rpart\")
install.packages(\"rpart.plot\")library(rpart)
library(rpart.plot)<\/pre>\n\n\n\nb) Fit Decision Tree Model<\/strong><\/h3>\n\n\n\n
tree_model <- rpart(HighValue ~ Age + Gender + ProductCategory,
data = train_data, method = \"class\")# Plot the tree
rpart.plot(tree_model, type=2, extra=104, fallen.leaves=TRUE)<\/pre>\n\n\n\nc) Make Predictions<\/strong><\/h3>\n\n\n\n
pred_tree <- predict(tree_model, test_data, type = \"class\")<\/pre>\n\n\n\nd) Evaluate Model Performance<\/strong><\/h3>\n\n\n\n
confusionMatrix(pred_tree, test_data$HighValue)<\/pre>\n\n\n\n3. Comparison of Logistic Regression and Decision Trees<\/strong><\/h1>\n\n\n\n
Feature: Logistic Regression vs Decision Tree<\/p>\n\n\n\n
Type: Parametric, linear vs Non-parametric, non-linear
Output: Probability of class vs Class labels
Interpretability: Coefficients show effect vs Tree structure is visual
Handling Non-Linearity: Limited without transformations vs Handles non-linear relationships
Robustness: Sensitive to outliers vs Less sensitive to outliers<\/p>\n\n\n\n4. Advantages of Classification Models in R<\/strong><\/h1>\n\n\n\n
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- Predict categorical outcomes from complex data<\/li>\n\n\n\n
- Identify important features that affect the target variable<\/li>\n\n\n\n
- Visualize decision-making with trees or probability outputs<\/li>\n\n\n\n
- Integrate easily with other R packages for evaluation and reporting<\/li>\n<\/ul>\n\n\n\n
5. Conclusion<\/strong><\/h1>\n\n\n\n
Logistic regression and decision trees are essential classification techniques in R. Logistic regression is ideal for binary outcomes with linear relationships, while decision trees handle complex, non-linear interactions and provide intuitive visual explanations. Mastering both methods allows you to build predictive models, evaluate performance, and make data-driven decisions effectively.<\/p>\n\n\n\n
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