Decision Trees, Explained

In this post we're going to discuss a commonly used machine learning model called decision tree. Decision trees are preferred for many applications, mainly due to their high explainability, but also due to the fact that they are relatively simple to set up and train, and the short time it takes to perform a prediction with a decision tree. Decision trees are natural to tabular data, and, in fact, they currently seem to outperform neural networks on that type of data (as opposed to images). Unlike neural networks, trees don't require input normalization, since their training is not based on gradient descent and they have very few parameters to optimize on. They can even train on data with missing values, but nowadays this practice is less recommended, and missing values are usually imputed.