Performance of Regression model and Classification model

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 Performance of Regression model and Classification model

In a machine learning Algorithm once the model is built, the next step is the use of various performance criteria to evaluate Machine learning Models.

Various performance Matrix/Criteria used for regression model and Classification models are different and are listed below.

Regression Model: In Regression analysis output is continuous value, therefore for Regression performance following methods are used

  1. Mean squared error (MSE)
  2. Mean average error(MAE)
  3. Root mean squared error (RMSE)
  4. R Square

Classification Model: In the classification model output is Discret form and for classification performance following methods are used

  1. Confusion matrix
  2. Accuracy
  3. Precision
  4. Recall (sensitivity)
  5. Specificity
  6.  ROC curve (AUC) ROC Area Under Curve is useful when we are not concerned about  whether the small dataset/class of dataset is positive or not, in contrast to F1 score where the class being positive is important.
  7. F-score(F1 score is useful when the size of the positive class is relatively small)

Performance metrics should be chosen based on the problem domain, project goals and objectives. 

For details see the next blog.


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