Model Evaluation Techniques
📘 Data Science
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📅 Nov 14, 2025
⏱ Estimated reading time: 1 min
Introduction
Model evaluation ensures that your machine learning model performs well on unseen data. It helps detect overfitting, underfitting, and model bias.
1. Train-Test Split
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
2. Cross Validation (K-Fold)
from sklearn.model_selection import KFold
kf = KFold(n_splits=5)
3. Confusion Matrix
from sklearn.metrics import confusion_matrix
confusion_matrix(y_test, predictions)
4. Classification Metrics
- Accuracy
- Precision
- Recall
- F1 Score
- ROC-AUC Score
5. Regression Metrics
- MSE
- RMSE
- MAE
- R² Score
Conclusion
Model evaluation is a key step in machine learning. It ensures that your model generalizes well and performs accurately in real-world environments.
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