Classification Algorithms

Here is a detailed summary of the Classification Algorithms in the uploaded document organized into bullet points and course notes. I have included key equations, concepts, and figures where possible.

Classification Overview

  • Problem: Given an object \(x\), predict its class label \(y\). Examples include identifying objects in computer vision, detecting fraudulent credit card transactions, and gene classification in personalized medicine.
  • Types:
    • Binary classification: \(y \in {0, 1}\)
    • Multiclass classification: \(y \in {1, \dots, n}\)
    • Regression: \(y \in \mathbb{R}\)

Evaluating Classifiers

  • Contingency Table: For binary classification, results are represented as:

    | | \(y = 1\) |\( y = -1 \)| |-------|-------|--------| | \( f(x) = 1 \) | TP | FP | |\( f(x) = -1 \)| FN | TN |

    TP: True Positive, FP: False Positive, FN: False Negative, TN: True Negative.

  • Accuracy: \[ \text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN} \] Accuracy is not ideal for imbalanced classes, which leads to focusing on precision and recall:

  • Precision: \( \frac{TP}{TP + FP} \)

  • Recall: \( \frac{TP}{TP + FN} \)

  • ROC Curve: Plots true positive rate (sensitivity) vs. false positive rate. A perfect classifier's curve passes through the point (0,1). The Area Under the Curve (AUC) is a metric summarizing the performance of the classifier: \[ AUC = P\left(\text{positive point gets higher score than negative}\right) \]

Cross-Validation and Bootstrapping

  • Cross-Validation: Split data into \(k\) subsets, train on \(k-1\), and test on the remaining subset. This is repeated \(k\) times.
  • Bootstrapping: Random sampling with replacement to create multiple training/test splits, averaging results over multiple iterations.
  • Parameter Tuning: Use internal cross-validation on training data to optimize model parameters without overfitting.

Nearest Neighbors (k-NN)

  • k-NN: Classifies a point based on the majority label of its \(k\) nearest neighbors.

    • Distance Metric: The Euclidean distance is often used: \[ d(x, x') = \sqrt{\sum_{i=1}^d (x_i - x'_i)^2} \]
    • Challenges: Selecting \(k\), runtime optimization, and handling high-dimensional data.

  • Mahalanobis Distance: \[ d_M(x, x') = \sqrt{(x - x')^\top \Sigma^{-1} (x - x')} \] where \(\Sigma\) is the covariance matrix of the dataset.

Naive Bayes Classifier

  • Bayes' Rule: \[ P(Y = y | X = x) = \frac{P(X = x | Y = y) P(Y = y)}{P(X = x)} \]

    • The classifier assumes conditional independence of features: \[ P(X | Y = y) = \prod_{j=1}^d P(X_j | Y = y) \]

  • Prediction: \[ \hat{y} = \arg\max_{y} P(Y = y) \prod_{j=1}^d P(X_j | Y = y) \]

    • Naive Bayes works well in practice despite the strong independence assumption.

Linear Discriminant Analysis (LDA)

  • Assumptions:

    • Data from each class is normally distributed with the same covariance matrix but different means \(\mu_0\) and \(\mu_1\).

  • Log-Likelihood Ratio: \[ f(x) = \log\left( \frac{P(Y=1|X=x)}{P(Y=0|X=x)} \right) \]

    • Linear discriminant: \( f(x) = w^\top x + b \), where \(w = (\mu_1 - \mu_0)^\top \Sigma^{-1}\).

Logistic Regression

  • Logistic Function: \[ f(z) = \frac{1}{1 + e^{-z}} \] where \(z = w^\top x + b\).

  • Training: Minimizing the logistic loss: \[ \mathcal{L}(w) = \frac{1}{n} \sum_{i=1}^n \log(1 + e^{-y_i (w^\top x_i)}) \]

    • The weights \(w\) are learned by gradient descent or other optimization techniques.

Decision Trees

  • Concept: Split the data recursively based on feature values to maximize information gain, using criteria like entropy and the Gini index:
    • Entropy: \[ H(D) = -\sum_{i=1}^m p_i \log_2(p_i) \]
    • Gini Index: \[ Gini(D) = 1 - \sum_{i=1}^m p_i^2 \]
  • Random Forests: Ensemble of decision trees, each built on random subsets of data and features.

Support Vector Machines (SVM)

  • Hard-Margin SVM: Finds the hyperplane that maximizes the margin between classes: \[ \min_w \frac{1}{2} |w|^2 \quad \text{s.t.} , y_i (w^\top x_i + b) \geq 1 \]
  • Soft-Margin SVM: Allows some misclassification using slack variables \(\xi\): \[ \min_w \frac{1}{2} |w|^2 + C \sum_{i=1}^n \xi_i \quad \text{s.t.} , y_i (w^\top x_i + b) \geq 1 - \xi_i \] where \(C\) controls the trade-off between margin size and misclassification.

Kernel Methods

  • Kernel Trick: Maps data into a higher-dimensional space to make it linearly separable. Common kernels include:
    • Linear Kernel: \( k(x, x') = x^\top x' \)
    • Polynomial Kernel: \( k(x, x') = (x^\top x' + c)^d \)
    • Gaussian (RBF) Kernel: \[ k(x, x') = \exp\left(- \frac{|x - x'|^2}{2\sigma^2}\right) \]

Figures/Diagrams

  1. ROC Curves: Visualization of model performance with true positive vs. false positive rates.
  2. Logistic Function: S-shaped curve representing the probability output from logistic regression.
  3. Decision Trees: Flowchart-like structures splitting data based on feature values.