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