- Lift Charts & Gain Charts: These are widely used in campaign targeting problems, to determine which decile can we target customers for a specific campaign. Also, it tells you how much response you can expect from the new target base.
- ROC Curve: The ROC curve is the plot between false positive rate and True Positive rate.
- Gini coefficient: This is the ratio of area between the ROC curve and the diagonal line & the area of the above triangle
- Cross Validation: splitting the data into two parts, where one part is used for “training” your model, and the second part is used to make predictions. By this you can test the model on the data that was “not seen” by it previously, and check how it could possibly behave with external data.
- Confusion Matrix: A table showing the number of predictions for each class compared to the number of instances that actually belong to each class. This is very useful to get an overview of the types of mistakes the algorithm made. This method shows accuracy, true positive, false positive, Sensitivity & specificity of the model.
- Root Mean Squared Error: This is the average amount of error made on the test set in the units of the output variable. This measure helps you get an idea on the amount a given prediction may be wrong on average. This is most popular in regression techniques.
How to evaluate Data Science models ?
Republished with author's permission from original post.