A soft skill that keeps coming to the forefront is the ability to explain complex machine learning algorithms to a non-technical person. An algorithm is the mathematical life force behind a model. What differentiates models are the algorithms they employ, but without a model, an algorithm is just a mathematical equation hanging out with nothing to do. An algorithm is what is used to train a model, all the decisions a model is supposed to take based on the given input, to give expected output.
Through this post you will learn all about decision trees, non-linearity, overfitting and variance, and ensemble models like Random Forest. A decision tree is a super simple structure we use in our heads everyday. It’s just a representation of how we make decisions, like an if-this-then-that game. Data has linearity of some sort when data points can be separated into groups by using a line (or linear plane). Non-linearity is really just the opposite of this. You can think of non-linear data and functions in a few different ways.