09 Regularization to Deal with Overfitting

04/19/20 • 15 min

In this episode with talk about regularization, an effective technique to deal with overfitting by reducing the variance of the model. Two techniques are introduced: ridge regression and lasso. The latter one is effectively a feature selection algorithm.

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08 Linear Regression: The Return of the Queen

In this episode I will try to convince you that Linear Regression is one of the most powerful Machine Learning algorithms. We will talk about common misconceptions, especially that Linear Regression is not able to model non-linear relationships. We also discuss how the myth of normality encourages many people to completely discard Linear Regression on non-normal data, when in reality, normality of the data has nothing to do with this assumption. Finally, I provide advice in how to check, but most importantly, how to fix any violated assumption in Linear Regression.

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10 Logistic Regression

Logistic regression is a very robust machine learning technique which can be used in three modes: binary, multinomial and ordinal. We talk about assumptions and some misconceptions. For example, people believe that because logistic regression fits only a linear separator in the expanded dimensional space it wouldn’t be able to fit a complex boundary in the original space. Also, people normally use either linear regression or multinomial logistic regression when they should be using ordinal logistic regression.