Glossary Archives - Page 3 of 10

True Positive (TP)

Written by Evispot. Posted in Glossary, t.

Assume a data set that includes examples of a class Non-default and examples of class Default. Assume further that you’re evaluating your model’s performance to predict examples of class Non-default.True positives is a field in the confusion...

Confusion Matrix

Written by Evispot. Posted in c, Glossary.

A confusion matrix helps to illustrate what kinds of errors a classification model is making. If you have a binary classifier model that distinguishes between a positive and a negative class, you can define the following 4 values depending on the...

Kolmogorov-Smirnov Statistics (KS)

Written by Evispot. Posted in Glossary, k.

The Kolmogorov–Smirnov test (KS) is a statistical test to check that two data samples come from the same distribution. The advantage of this test is that it’s non-parametric in nature, therefore it is distribution agnostic. Thus, we are not concerned...

Mean Absolute Error (MAE)

Written by Evispot. Posted in Glossary, m.

The mean absolute error (MAE) is an average of the absolute errors. The MAE units are the same as the predicted target, which is useful for understanding whether the size of the error is of concern or not. The smaller the MAE the better the model’s...

Mean Squared Error (MSE)

Written by Evispot. Posted in Glossary, m.

The MSE metric measures the average of the squares of the errors, that is the average squared difference between the estimated values and the actual value. The MSE is a measure of the quality of an estimator—it is always non-negative, and values...

Log loss

Written by Evispot. Posted in Glossary, l.

Log loss, also called logistic regression loss or cross-entropy loss, is defined on probability estimates.The metric looks at how well a model can classify a binary target, log loss evaluates how close a model’s predicted values (uncalibrated probability...

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