Decision trees, SVM, and classification

Supervised Learning

Learn from labeled data to make predictions on new examples

Linear Regression Visualization

Interactive demonstration of linear regression algorithm finding the best mPn line through data points.

Controls
Algorithm Information

Objective: Find the best line (y = mx + b) that minimizes the sum of squared u14.

Method: Uses gradient descent or normal equation to optimize parameters.

Use Cases: Predicting house prices, stock prices, temperature forecasting.

Classification with Decision Boundary

Visualize how different classification algorithms separate data into different classes.

Classification Controls
Classification Algorithms

Logistic Regression: Uses sigmoid function to model probability of class membership.

SVM: Finds optimal hyperplane that maximally separates classes.

K-NN: Classifies based on majority vote of k nearest l31.

Key Concepts

  • Training Data: Labeled examples used to learn patterns
  • Features: Input variables used for prediction
  • Labels: Known output values for training examples
  • Model: Mathematical function learned from data

Algorithm Types

Regression
Predicts continuous values
  • Linear Regression
  • Polynomial Regression
  • Ridge/Lasso Regression
Classification
Predicts discrete categories
  • Logistic Regression
  • Decision Trees
  • Support Vector Machines
  • Random Forest

Performance metrics

Mean Squared Error: -

R² Score: -

Accuracy: -