Predictive Analytics

4.

Classification Modeling

4.1.

Binary Classification

4.1.1.

Logistic Regression

4.1.1.1.

4.1.1.2.

Maximum Likelihood Estimation

4.1.1.3.

Odds Ratio Interpretation

4.1.1.4.

Model Diagnostics

4.1.2.

Linear Discriminant Analysis

4.1.2.1.

Bayes' Theorem Foundation

4.1.2.2.

Discriminant Functions

4.1.2.3.

Assumptions and Limitations

4.1.3.

Quadratic Discriminant Analysis

4.1.3.1.

Non-linear Decision Boundaries

4.1.3.2.

Covariance Matrix Estimation

4.2.

Multi-class Classification

4.2.1.

Multinomial Logistic Regression

4.2.1.1.

Softmax Function

4.2.1.2.

Reference Category Selection

4.2.1.3.

Parameter Interpretation

4.2.2.

One-vs-Rest Strategy

4.2.2.1.

Binary Classifier Extension

4.2.2.2.

Decision Function Combination

4.2.3.

One-vs-One Strategy

4.2.3.1.

Pairwise Classification

4.2.3.2.

Voting Mechanisms

4.3.

Instance-based Learning

4.3.1.

k-Nearest Neighbors

4.3.1.1.

Distance Metrics

4.3.1.1.1.

Euclidean Distance

4.3.1.1.2.

Manhattan Distance

4.3.1.1.3.

Minkowski Distance

4.3.1.1.4.

Cosine Similarity

4.3.1.2.

k Parameter Selection

4.3.1.3.

Weighted Voting Schemes

4.3.1.4.

Curse of Dimensionality

4.4.

Probabilistic Classifiers

4.4.1.

4.4.1.1.

Conditional Independence Assumption

4.4.1.2.

Gaussian Naive Bayes

4.4.1.3.

Multinomial Naive Bayes

4.4.1.4.

Bernoulli Naive Bayes

4.4.1.5.

Laplace Smoothing

4.5.

Support Vector Machines

4.5.1.

4.5.1.1.

Maximum Margin Principle

4.5.1.2.

Support Vector Identification

4.5.1.3.

Soft Margin Classification

4.5.2.

4.5.2.1.

4.5.2.2.

Polynomial Kernels

4.5.2.3.

Radial Basis Function Kernels

4.5.2.4.

Sigmoid Kernels

4.5.3.

SVM Parameter Tuning

4.5.3.1.

C Parameter Selection

4.5.3.2.

Kernel Parameter Optimization

4.5.3.3.

Cross-validation Strategies

4.6.

Tree-based Classification

4.6.1.

4.6.1.1.

Splitting Criteria

4.6.1.1.1.

4.6.1.1.2.

Entropy and Information Gain

4.6.1.1.3.

Classification Error

4.6.1.2.

Tree Construction Algorithms

4.6.1.2.1.

4.6.1.2.2.

4.6.1.2.3.

4.6.1.3.

Pruning Techniques

4.6.1.3.1.

4.6.1.3.2.

4.6.1.3.3.

Cost Complexity Pruning

4.6.2.

Handling Categorical Features

4.6.2.1.

4.6.2.2.

Multi-way Splits

4.6.2.3.

Optimal Subset Selection

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