Regression_AlgorithmsWithProsandCons.md

1. Linear Regression:

   • Pros:
     • Simple and easy to understand.
     • Fast training and prediction.
     • Provides coefficients that indicate feature importance.
   • Cons:
     • Assumes a linear relationship between features and the target variable.
     • Sensitive to outliers.

2. Ridge Regression (L2 Regularization):

   • Pros:
     • Handles multicollinearity by adding regularization term.
     • Reduces the impact of irrelevant features.
   • Cons:
     • Requires tuning of the regularization parameter.
     • Does not perform feature selection.

3. Lasso Regression (L1 Regularization):

   • Pros:
     • Performs feature selection by setting some coefficients to zero.
     • Handles multicollinearity.
   • Cons:
     • May not work well with a large number of features.
     • Requires tuning of the regularization parameter.

4. Elastic Net Regression:

   • Pros:
     • Combines L1 and L2 regularization.
     • Balances the advantages of Ridge and Lasso.
   • Cons:
     • Requires tuning of two regularization parameters.

5. Decision Tree Regression:

   • Pros:
     • Nonlinear relationships can be captured.
     • No need for feature scaling.
     • Handles both numerical and categorical data.
   • Cons:
     • Prone to overfitting, especially on small datasets.
     • Instability: small changes in data can lead to different tree structures.

6. Random Forest Regression:

   • Pros:
     • Reduces overfitting compared to a single decision tree.
     • Handles a large number of features and complex relationships.
     • Provides feature importance scores.
   • Cons:
     • May be computationally expensive.
     • Less interpretable than a single decision tree.

7. Gradient Boosting Regression (e.g., XGBoost, LightGBM):

   • Pros:
     • High predictive accuracy.
     • Handles complex relationships and interactions.
     • Robust to outliers.
   • Cons:
     • More complex, requires tuning of hyperparameters.
     • May be sensitive to overfitting.

8. Support Vector Regression:

   • Pros:
     • Effective in high-dimensional spaces.
     • Robust to outliers.
     • Versatile due to different kernel functions.
   • Cons:
     • May require careful tuning of hyperparameters.
     • Memory-intensive for large datasets.

9. K-Nearest Neighbors Regression:

   • Pros:
     • No assumption about the underlying data distribution.
     • Simple and easy to understand.
   • Cons:
     • Sensitive to outliers.
     • Computationally expensive during prediction for large datasets.

10. Huber Regression:

    • Pros:
      • Robust to outliers.
      • Balances the advantages of Mean Squared Error and Mean Absolute Error.
    • Cons:
      • Requires tuning of a hyperparameter