Data science validate the accuracy of a machine learning model using several techniques to ensure the model performs well on unseen data. Here are key methods:
1. Train-Test Split
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The dataset is split into trainiData scienceng and testing sets (commonly 80:20 or 70:30).
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The model is trained on the training set and evaluated on the testing set.
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Helps check if the model is overfitting or underfitting.
2. Cross-Validation
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Most commonly, k-fold cross-validation is used.
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The dataset is divided into k subsets, and the model is trained and validated k times, each time using a different fold as the validation set.
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Provides a more reliable estimate of model performance.
3. Confusion Matrix
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For classification models, it shows True Positives, True Negatives, False Positives, and False Negatives.
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Helps calculate accuracy, precision, recall, and F1 score.
4. Performance Metrics
Depending on the task:
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Classification: Accuracy, Precision, Recall, F1 Score, ROC-AUC
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Regression: Mean Squared Error (MSE), Mean Absolute Error (MAE), R² Score
5. Hold-Out Validation / Validation Set
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In addition to the train-test split, a validation set can be used to tune hyperparameters before final testing.
6. Residual Analysis
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Used in regression to analyze the difference between predicted and actual values.
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Helps detect patterns that suggest model bias or variance issues.
7. Out-of-Sample Testing
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Apply the model to new or external datasets that were not involved in model training to test generalization ability.