Classical Supervised Learning

Beyond logistic regression, advanced analytics and ML methods encompass Naive Bayes, KNN, SVM, tree-based models, ensemble methods (Random Forest), specialized transformations like WoE/IV, and Market Basket Analysis. They broaden the data-driven toolkit for classification, segmentation, and association rule discovery.

SUPERVISED LEARNING

High-level algorithms such as Naive Bayes, K-Nearest Neighbors, and Support Vector Machines expand basic classification beyond logistic regression. They use probability, proximity, or optimal margin principles to address diverse data patterns, offering flexible approaches to label prediction while requiring careful feature scaling and parameter tuning.

NAIVE BAYES CLASSIFICATION

Learning Objectives

• Apply Bayes’ theorem for categorical/continuous features (GaussianNB, MultinomialNB)

• Understand conditional independence assumptions and Laplace smoothing

• Evaluate with confusion matrix, ROC, precision/recall

Indicative Content

• Bayes Theorem

  • Posterior ∝ likelihood × prior

• Naive Assumption

  • Feature independence

• Implementation

  • scikit-learn NB variants, confusion matrix, AUC

K-NEAREST NEIGHBOURS (KNN) CLASSIFICATION

Learning Objectives

• Classify based on distance to labeled neighbors

• Pick K using cross-validation or heuristics

• Scale data to avoid magnitude bias

Indicative Content

• Distance Metrics

  • Euclidean, Manhattan

• Voting

  • Majority or distance-weighted

• Implementation

  • KNeighborsClassifier, checking performance metrics

SUPPORT VECTOR MACHINES (SVM)

Learning Objectives

• Find optimal margin hyperplane for linear or kernel-based separation

• Adjust parameters (C, gamma) for best performance

• Measure success with confusion matrix, ROC, AUC

Indicative Content

• Margin Maximization

  • Support vectors, slack variables

• Kernels

  • Linear, RBF, polynomial

• Implementation

  • sklearn.svm.SVC, tuning (C, gamma)

TOOLS & METHODOLOGIES (CLASSICAL SUPERVISED LEARNING)

• Python

  • scikit-learn for Naive Bayes, KNN, SVM

• Evaluation

  • Confusion matrix, ROC/AUC, precision/recall

• Workflow

  • Data prep → model training (with feature scaling) → performance checks (metrics, hyperparameter tuning)