Evaluate performance of a ML model

Evaluating the performance of a machine learning model is important to assess its accuracy,
generalization ability, and limitations.

Here are some common evaluation metrics for different types of ML Model problems:

Regression Model problems:
Mean Absolute Error (MAE),
Mean Squared Error (MSE),
Root Mean Squared Error (RMSE)

Binary classification Model problems:
Accuracy, Precision, Recall, F1-Score,
AUC-ROC (Area Under the Receiver Operating Characteristic Curve)

Multi-class classification Model problems:
Confusion Matrix, Accuracy,
Precision, Recall, F1-Score

Clustering Model problems:
Silhouette Score, Calinski-Harabasz Index,
Davies-Bouldin Index

Anomaly detection Model problems:
Precision, Recall, F1-Score,
Area Under the Precision-Recall Curve (AUC-PR)

These metrics help to evaluate the performance of the model
and determine its strengths and weaknesses.

The specific metric used will depend on the problem,
the data, and the desired outcome.

It’s important to evaluate a model using multiple metrics
to get a comprehensive view of its performance.

In addition to using evaluation metrics,
it’s also important to validate the model using techniques
such as cross-validation and
testing on hold-out data to assess its generalization ability.