Test data set

The test data set is used for the final stage scoring and is the data set for which model metrics will be computed against. Test set predictions will be available at the end of the experiment. This data set is not used during training of the modeling...

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Target Leakage

Target leakage, sometimes called data leakage, is one of the most difficult problems when developing an AI model. It happens when you train your algorithm on a data set that includes information that would not be available at the time of prediction...

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Overfitting

Overfitting is the phenomenon of a model not performing well, i.e., not making good predictions, because it captured the noise as well as the signal in the training set. In other words, the model is generalizing too little and instead of just characterizing...

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Underfitting

Underfitting is the phenomenon of a model not performing well, i.e., not making good predictions, because it wasn’t able to correctly or completely capture the signal in the training set. In other words, the model is generalizing too much, to the...

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Machine learning in credit decisions

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