When working on a machine learning assignment, evaluating model performance is crucial to ensure accuracy and reliability. Choosing the right evaluation metrics depends on the type of problem—classification, regression, or clustering. Here are some key metrics that every student should use in their assignments:
1. Accuracy, Precision, and Recall (For Classification Models)
Accuracy: Measures the overall correctness of predictions but may not be ideal for imbalanced datasets.
Precision & Recall: Precision evaluates how many positive predictions were correct, while recall measures how well the model captures actual positives.
2. F1-Score
F1-score is the harmonic mean of precision and recall, balancing both metrics, especially in cases where data is imbalanced.
3. Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) (For Regression Models)
MSE: Measures the average squared difference between actual and predicted values.
RMSE: The square root of MSE, providing a more interpretable error measurement in the same unit as the target variable.
4. R-Squared (R²) Score
Indicates how well the independent variables explain the variance in the dependent variable. A higher R² score means better performance.
5. Confusion Matrix
A confusion matrix provides a detailed breakdown of true positives, true negatives, false positives, and false negatives, helping to assess classification model performance.
6. Cross-Validation
Using techniques like k-fold cross-validation ensures that your model's performance is reliable across different data subsets.
7. ROC-AUC Score
For classification models, the Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) score help measure how well the model distinguishes between classes.
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By understanding these key evaluation metrics, you can enhance your machine learning assignments and ensure well-validated models. Happy coding! 🚀