Contents
How does scaling affect logistic regression?
The performance of logistic regression did not improve with data scaling. The reason is that, if there predictor variables with large ranges that do not effect the target variable, a regression algorithm will make the corresponding coefficients ai small so that they do not effect predictions so much.
How do you predict using logistic regression?
A logistic regression model will try to guess the probability of belonging to one group or another. The logistic regression is essentially an extension of a linear regression, only the predicted outcome value is between [0, 1].
When should we use Logistic Regression?
Logistic regression is applied to predict the categorical dependent variable. In other words, it’s used when the prediction is categorical, for example, yes or no, true or false, 0 or 1. The predicted probability or output of logistic regression can be either one of them, and there’s no middle ground.
Do you need to scale variables in logistic regression?
For linear/logistic regression without regularization you need to scale features only if you’d like to interpret/compare weights after fitting. Otherwise features with higher values will possibly have smaller weights than other ones.
What do the coefficients mean in logistic regression?
It is worth noting here that in logistic regression your coefficients indicate the effect of a one-unit change in your predictor variable on the log odds of ‘success’. The effect of transforming a variable (such as by standardizing or normalizing) is to change what we are calling a ‘unit’ in the context of our model.
Is there best practice to normalize the features when doing logistic regression?
Regularization makes the predictor dependent on the scale of the features. If so, is there a best practice to normalize the features when doing logistic regression with regularization? Yes. The authors of Elements of Statistical Learning recommend doing so.
Do you use lasso or ridge regression in logistic regression?
If you use logistic regression with LASSO or ridge regression (as Weka Logistic class does) you should. As Hastie,Tibshirani and Friedman points out (page 82 of the pdf or at page 63 of the book): The ridge solutions are not equivariant under scaling of the inputs, and so one normally standardizes the inputs before solving.