Machine learning algorithms fuel machine learning models. They consist of three parts: a decision process, an error function and a model optimization process.
Supervised learning is an approach to machine learning that uses labeled data sets to train algorithms in order to properly classify data and predict outcomes.
R is a programming language that provides access to a variety of statistical and graphical techniques while producing plots. R is widely used in data science.
Feature importance involves calculating a score for all input features in a machine learning model to determine which ones are the most important. Here’s how to do it.
Python is a general-purpose, object-oriented programming language that’s popular in data science thanks to its rich libraries offering deep learning capabilities.
