Ensemble Learning with Support Vector Machines and Decision Trees

Let’s pretend for a second that Machine Learning models are real human beings: none of them is perfect (besides you, of course). Some models could be too anxious, someone too jealous, someone too arrogant. The real magic happens when you fall in love with someone that is able to see your weak points and helps you improve them, and he/she emphasises your good sides. This is the exact idea of <a href="https://en.wikipedia.org/wiki/Ensemble_learning" rel="noopener ugc nofollow" target="_blank">Ensemble Learning.</a> In fact, it is based on the idea that a wide collection of models is able to perform better than each model that is taken in isolation. It may sound complicated at a first sight, so let’s proceed with a real dataset and learn it by example. <a href="https://towardsdatascience.com/ensemble-learning-with-support-vector-machines-and-decision-trees-88f8a1b5f84b">Read More</a>