Predictive Power

Quality indicator of a classification predictive model.

The predictive power measures the ability of your predictive model to predict the values of the target variable using the influencers present in the training data source.

How to interpret the indicator?

The predictive power indicator takes a value between 0% and 100%. This value should be as close as possible to 100%, without being equal to 100%.

A predictive power of 1 is a hypothetically perfect predictive model, where the influencers are capable of accounting for 100% of information in the target variable. In practice, however, this is usually an indication that an influencer, that is 100% correlated with the target variable, was not excluded from the data source analyzed. A good practice would be to exclude this influencer when you define the settings of your predictive model.

A predictive power of 0 is a purely random predictive model with no predictive power.

Tip

To improve the predictive power of a predictive model, try adding new influencers to the training data source.

Example

A predictive model with a predictive power of 79% is capable of accounting for 79% of the variation in the target variable using the influencers in the data source analyzed.

No exact threshold exists to separate a “good” predictive model from a “bad” predictive model in terms of predictive power as this depends on your business case. The predictive model of a customer-based scenario can be considered "good" with a predictive power of 40, while the predictive model of a finance-based scenario usually requires a predictive power above 70 to be considered "good".