Author: Evispot

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...

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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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Gini

Written by Evispot. Posted in g, Glossary.

The Gini coefficient is a well-established method to quantify the inequality among values of a frequency distribution, and can be used to measure the quality of a binary classifier. Gini is measured between 0 and 1. A Gini index of 0 expresses perfect...

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ROC curve

Written by Evispot. Posted in Glossary, r.

A receiver operating characteristic (ROC), or simply ROC curve, is a graphical plot which illustrates the performance of a binary classifier system as its discrimination threshold is varied. It is created by plotting the fraction of true positives...

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Undersampling

Written by Evispot. Posted in Glossary, u.

It’s the process of balancing a data set by discarding examples of the overrepresented class so that each has the same amount of examples. A balanced data set allows a model to learn equal amounts of characteristics from each one of the classes...

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Hyperparameter

Written by Evispot. Posted in Glossary, h.

Hyperaparameter is a parameter whose value is used to control the learning process of a specific AI model. A hyperparameter has to be set / fixed before starting the training process.

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