Machine Learning with Scikit-Learn

6.

Supervised Learning: Classification

6.1.

Binary Classification

6.1.1.

Problem Definition

6.1.2.

Decision Boundaries

6.1.3.

Threshold Selection

6.2.

Multi-class Classification

6.2.1.

One-vs-Rest Strategy

6.2.2.

One-vs-One Strategy

6.2.3.

Native Multi-class Algorithms

6.3.

Multi-label Classification

6.3.1.

Problem Definition

6.3.2.

Evaluation Metrics

6.3.3.

6.4.

Linear Models for Classification

6.4.1.

Logistic Regression

6.4.1.1.

Sigmoid Function

6.4.1.2.

Maximum Likelihood Estimation

6.4.1.3.

Coefficient Interpretation

6.4.1.4.

Regularization Options

6.4.2.

Linear Discriminant Analysis

6.4.2.1.

6.4.2.2.

Dimensionality Reduction

6.4.3.

Quadratic Discriminant Analysis

6.4.3.1.

Non-linear Boundaries

6.4.4.

Stochastic Gradient Descent Classifier

6.4.4.1.

Online Learning

6.4.4.2.

6.4.4.3.

6.5.

Support Vector Machines

6.5.1.

6.5.1.1.

Maximum Margin Principle

6.5.1.2.

Support Vectors

6.5.1.3.

6.5.2.

Kernelized SVMs

6.5.2.1.

6.5.2.2.

Polynomial Kernel

6.5.2.3.

6.5.2.4.

6.5.3.

Parameter Tuning

6.5.3.1.

6.5.3.2.

Gamma Parameter

6.5.3.3.

Kernel Parameters

6.6.

K-Nearest Neighbors Classifier

6.6.1.

6.6.2.

Distance Metrics

6.6.2.1.

Euclidean Distance

6.6.2.2.

Manhattan Distance

6.6.2.3.

Minkowski Distance

6.6.3.

6.6.4.

Weighted Voting

6.6.5.

Curse of Dimensionality

6.7.

Naive Bayes Classifiers

6.7.1.

6.7.2.

Independence Assumption

6.7.3.

6.7.3.1.

Continuous Features

6.7.3.2.

Gaussian Distribution

6.7.4.

6.7.4.1.

Discrete Features

6.7.4.2.

Text Classification

6.7.5.

6.7.5.1.

Binary Features

6.7.6.

6.7.6.1.

Imbalanced Datasets

6.8.

Tree-Based Models

6.8.1.

Decision Tree Classifier

6.8.1.1.

Tree Construction

6.8.1.2.

Splitting Criteria

6.8.1.2.1.

6.8.1.2.2.

6.8.1.2.3.

6.8.1.3.

Pruning Techniques

6.8.1.4.

Feature Importance

6.8.2.

Ensemble Methods

6.8.2.1.

Random Forest Classifier

6.8.2.1.1.

Bootstrap Aggregating

6.8.2.1.2.

Feature Randomness

6.8.2.1.3.

Voting Mechanism

6.8.2.2.

Extra Trees Classifier

6.8.2.3.

Gradient Boosting Classifier

6.8.2.3.1.

Sequential Learning

6.8.2.3.2.

6.8.2.3.3.

6.8.2.4.

AdaBoost Classifier

6.8.2.4.1.

Adaptive Boosting

6.8.2.4.2.

Sample Weighting

6.8.2.5.

Histogram-based Gradient Boosting

6.9.

Neural Network Models

6.9.1.

6.9.1.1.

Multi-layer Perceptron

6.9.1.2.

6.9.1.3.

Activation Functions

6.9.1.4.

Backpropagation

6.9.1.5.

6.10.

Ensemble Voting

6.10.1.

6.10.2.

6.10.3.

VotingClassifier

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7. Model Evaluation and Metrics