Which is better, logistic regression or decision tree?

Tiara Williamson Answered

The connection between the category attribute value and one or more independent factors is measured using Logistic Regression. It is useful when the outcome of a target variable can only be one of two potential types. To predict the target variable classes, Binary Logit Regression Classification employs one or more predictor variables that might be either continuous or categorical.

When it comes to prediction accuracy, Logistic Regression is typically superior. However, you may test this on your own data set by performing cross-validation. Note that comparing the two methods is difficult since Logistic Regression requires you choose the interaction terms manually, whereas Regression Trees don’t.

In terms of usability, Logistic Regression has the benefit of having more software support and producing an inference in the form of a simple table that can be pasted into a spreadsheet or document. Regression Trees may be trained with a few mouse clicks, but Logistic Regression needs you to pick variables, transformations, and interactions. Multinomial Logistic Regression, in particular, necessitates very sophisticated variable selection when there are more than two answer classes.

In terms of output interpretability, Logistic Regression delivers single values for each predictor, along with confidence intervals. Regression Trees give clear-cut categorization rules that appeal to audiences that are not as numerate.

Aside from the advantage of being much older, Logistic Regression Machine Learning is quite fascinating and accomplishes some things far better than a Decision Tree when you possess a lot of time and knowledge.

A Decision Tree’s second restriction is that it is quite costly in terms of the sample size. Each time it separates the data using only a predictor, the leftover sample size shrinks until there is no longer enough data to discover other predictors. However, some of these additional factors are likely to remain useful. In contrast, Logistic Regression considers the simultaneous effects of all variables and can do significantly better with small sample size.

Taking their differences into consideration, when conducting an academic analysis and you want to draw conclusions as to what affects what, it is safe to say Logistic Regression is frequently far superior to Decision Trees. But, if the purpose is to make a forecast or characterize the data, Logistic Regression is frequently a bad choice.