ML Model Performance
ML Model ability to make accurate predictions or decisions on unseen data.
Notes
Understanding theperformance of ML Models involves evaluating them on multiple dimensions, such as ML Accuracy, ML Precision, ML Recall, and F1 score.
Techniques like Cross-Validation help ensure that a model generalizes well to unseen data, while monitoring for issues like ๐ Overfitting is crucial to maintaining robust performance.
TakeAways
- ๐งฎ Evaluation Metrics are essential for evaluating models.
- ๐ก Common issues impacting performance are ๐ Overfitting and ๐ข Underfitting
- ๐ Cross-Validation and AUC-ROC curve are standard techniques to assess a modelโs robustness.
Process
- ๐ Define Metrics: Choose appropriate metrics based on the problem type (๐ท Classification, ๐ Regression).
- ๐ป Collect Data: Gather relevant data for training and testing.
- โ๏ธ Train Model: Fit the model using the training data.
- ๐ Evaluate: Assess the modelโs performance using validation or test data.
Thoughts
- Bias-Variance Tradeoff: Balancing between ๐ข Underfitting (high bias) and ๐ Overfitting (high variance).
- Regularization: Techniques like L1/L2 regularization to prevent overfitting.