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Introduction to Model Evaluation in Regression

  • Model evaluation in regression measures how accurately a regression model predicts numerical values using various performance metrics.

  • What is Model Evaluation?

    • Model evaluation measures the accuracy and reliability of a regression model.

    • It answers questions like:

      • Are the predictions close to the actual values?

      • Is the model overfitting or underfitting?

    • Helps in choosing the best model among multiple candidates.


    Why is it Important?

    1. Performance Measurement: Understand how good the model is.

    2. Model Comparison: Compare different regression algorithms.

    3. Error Identification: Identify patterns of errors in predictions.

    4. Model Improvement: Fine-tune hyperparameters or features.


    Regression vs Classification Evaluation

    Feature

    Regression

    Classification

    Output Type

    Continuous (e.g., price, salary)

    Categorical (e.g., pass/fail)

    Metrics

    MAE, MSE, RMSE, R²

    Accuracy, Precision, Recall, F1-score

    Goal

    Minimize prediction error

    Maximize correct classification


    Key Regression Metrics (Overview)

    1. Mean Absolute Error (MAE) – Average of absolute differences between predicted and actual values

    2. Mean Squared Error (MSE) – Average of squared differences (penalizes large errors)

    3. Root Mean Squared Error (RMSE) – Square root of MSE (same units as target variable)

    4. R² Score (Coefficient of Determination) – Proportion of variance explained by the model


    Visual Understanding

    • Predicted vs Actual Plot: Helps to see how close predictions are to real values

    • Residual Plot: Shows errors (difference between actual and predicted)

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