Machine_Learning_Algorithms__thoery.pptx

It contains the description of algorithms

1. Regression Algorithm: Linear Regression **Equation:** y = mx + b Linear regression models the relationship between a dependent variable (y) and an independent variable (x) using a straight line.

2. Linear Regression - Blank Plot

3. Regression Algorithm: Polynomial Regression **Equation:** y = a_nx^n + a_{n-1}x^{n-1} + ... + a_1x + a_0 Polynomial regression fits a nonlinear relationship using higher-degree polynomial functions.

4. Polynomial Regression - Blank Plot

5. Regression Algorithm: Ridge Regression **Equation:** y = X\beta + \lambda ||\beta||^2 Ridge regression adds a penalty term (λ) to linear regression to reduce overfitting and multicollinearity.

6. Ridge Regression - Blank Plot

7. Classification Algorithm: Logistic Regression **Equation:** P(y=1) = \frac{1}{1 + e^{-(b_0 + b_1x)}} Logistic regression predicts binary outcomes using the sigmoid function to map values between 0 and 1.

8. Logistic Regression - Blank Plot

9. Classification Algorithm: Decision Tree **Equation:** Uses entropy and information gain Decision trees classify data points by recursively splitting based on feature values using entropy or Gini impurity.

10. Decision Tree - Blank Plot

11. Classification Algorithm: Support Vector Machine (SVM) **Equation:** f(x) = w^T x + b SVM finds the optimal hyperplane that maximizes the margin between different classes.

12. Support Vector Machine (SVM) - Blank Plot