In the previous post, we discussed the Mean Absolute Deviation and how it can be a useful tool for evaluating the performance of a machine learning model when the data is affected by extreme values. Another very interesting measure is the R-squared , which unlike the Mean Squared Error, is a proportion and always takes values between zero and one.

We discussed the MSE and how it can be decomposed into prediction vari- ance and bias. We also highlighted that its major drawback is its inability to handle outliers. In these cases, it might be wise to employ a performance measure that accounts for this particular data structure. An alternative could be the Mean Absolute Deviation (MAD).

In the context of statistical or machine learning models, the use of met- rics is essential to evaluate the performance of our estimates. Let’s explore the main features of the Mean Square Error (MSE).

Estratto di una lezione tenuta da me per il 30° CORSO DI METODOLOGIA STATISTICA PER LA RICERCA BIOLOGICA DI BASE ED APPLICATA tenuto dalla Società Italiana di Biometria

The purpose of this Markdown is to highlight the methodological and conceptual differences between two of the most important techniques in Statistical Learning. Often, people do not know how and why to use Ridge and Lasso regression. This script is a small contribution to help clarify the issue.

The purpose of this Markdown is to highlight the methodological and conceptual differences between two of the most important techniques in Statistical Learning. Often, people do not know how and why to use Ridge and Lasso regression. This script is a small contribution to help clarify the issue.