Tree-based algorithms suffer from severe limitations when applied to forecasting problems. They can't predict beyond observed training data points values. However, not everything is lost. There are some alternative approaches to improve the performance of the tree-based algorithm under such scenarios. [5min read]
The processeses and the methods followed in Academia for evaluating a Machine Learning Model are different from the approaches used by the Industry. Why? [4min read]
Feature selection is a topic any machine learning practicioner should master. There are plenty strategies for performing feature selection. Some more useful than others. Some with more limitation than benefits. Here, I mention the most common approaches for feature selection using information collected from articles, books and research papers. [5 min read]
