Ensembles & Market Basket Analysis

Combining multiple trees reduces variance (Random Forest), while Market Basket Analysis extracts frequent itemsets from transactional data. These methods extend classification or association tasks to handle diverse retail or e-commerce scenarios, uncovering hidden patterns and improving accuracy through ensemble voting or association rules.

RANDOM FOREST

Learning Objectives

• Ensemble multiple trees with bagging to reduce variance

• Randomly select features at each split for de-correlation

• Interpret variable importance and out-of-bag (OOB) error

Indicative Content

• Bagging Concept

  • Voting/averaging multiple bootstrap-sampled trees

• max_features, n_estimators

  • Tuning forest size and feature subset

• Implementation

  • RandomForestClassifier/RandomForestRegressor, analyzing feature importances, OOB scores

MARKET BASKET ANALYSIS

Learning Objectives

• Discover frequent itemsets using Apriori

• Understand support, confidence, lift to filter relevant association rules

• Apply to cross-selling, store layout, or product bundling

Indicative Content

• Apriori Algorithm

  • Generating frequent itemsets above min_support

• Association Rules

  • A → B, measure with confidence and lift

• Implementation

  • mlxtend.frequent_patterns (apriori, association_rules)

TOOLS & METHODOLOGIES (ENSEMBLES & MARKET BASKET ANALYSIS)

• Python

  • RandomForestClassifier, RandomForestRegressor for ensembles

  • mlxtend.frequent_patterns for association rule mining

• Evaluation

  • Feature importance, OOB error, rule metrics (lift, confidence)

• Workflow

  • Construct ensemble → tune hyperparameters → interpret results → for association rules, define thresholds → generate insights