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Model Evaluation

Model evaluation in machine learning is the process of assessing the performance of a trained model to ensure it generalizes well to unseen data. It involves using various metrics and techniques to measure how well the model performs on a given task. Below are key aspects of model evaluation:

1. Train-Test Split​

  • Training Set: Used to train the model.
  • Validation Set: Used to tune hyperparameters.
  • Test Set: Used to evaluate model performance on unseen data.

2. Evaluation Metrics​

For Classification:​

  • Accuracy:
  • Precision: Measures how many of the predicted positives are actually positive.
  • Recall (Sensitivity): Measures how many actual positives were correctly identified.
  • F1 Score: Harmonic mean of precision and recall.
  • ROC-AUC: Measures the trade-off between sensitivity and specificity.
  • Confusion Matrix: Shows true positives, true negatives, false positives, and false negatives.

For Regression:​

  • Mean Absolute Error (MAE): Measures average absolute differences between predictions and actual values.
  • Mean Squared Error (MSE): Penalizes larger errors more than MAE.
  • Root Mean Squared Error (RMSE): Square root of MSE for interpretable error values.
  • R² Score (Coefficient of Determination): Indicates how well the model explains variance in the data.

3. Cross-Validation​

  • K-Fold Cross-Validation: Splits data into (k) parts and iteratively trains the model on (k-1) parts while testing on the remaining part.
  • Leave-One-Out Cross-Validation (LOOCV): Uses each data point as a separate test set.
  • Stratified K-Fold: Ensures class distribution is preserved in each fold.

4. Bias-Variance Tradeoff​

  • High Bias (Underfitting): The model is too simple and fails to capture patterns.
  • High Variance (Overfitting): The model is too complex and performs well on training data but poorly on test data.

5. Hyperparameter Tuning​

  • Grid Search: Exhaustively tests combinations of hyperparameters.
  • Random Search: Randomly selects combinations of hyperparameters.
  • Bayesian Optimization: Uses probabilistic models to find the best hyperparameters.

6. Performance Monitoring​

  • Learning Curves: Show how training and validation error evolve.
  • Precision-Recall Curve: Useful for imbalanced classification problems.
  • Feature Importance: Determines which features contribute the most.
citation

Model Evaluation Techniques in Machine Learning​

Model evaluation is the process of using different evaluation metrics to understand a machine learning model’s performance, as well as its strengths and weaknesses.

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https://medium.com/@fatmanurkutlu1/model-evaluation-techniques-in-machine-learning-8cd88deb8655

citation

Machine Learning Model Evaluation​

Model evaluation is a process that uses some metrics which help us to analyze the performance of the model. Think of training a model like teaching a student. Model evaluation is like giving them a test to see if they truly learned the subject—or just memorized answers. It helps us answer:

Did the model learn patterns? Will it fail on new questions? ...

https://www.geeksforgeeks.org/machine-learning-model-evaluation/