Chapter 2
Data Cleaning and Advanced Machine Learning
Section 4
Training Classification Models
As we've already seen in the previous lesson, using libraries such as scikit-learn and platforms such as Jupyter, predictive models can be trained in just a few lines of code. This is possible by abstracting away the difficult computations involved with optimizing model parameters. In other words, we deal with a black box where the internal operations are hidden instead. With this simplicity also comes the danger of misusing algorithms, for example, by overfitting during training or failing to properly test on unseen data. We'll show how to avoid these pitfalls while training classification models and produce trustworthy results with the use of k-fold cross validation and validation curves. This video covers: - Regression and Classification - Binary and Multi-class Classification - Classification Models - Demo on Training Two-feature Classification Models with Scikit-learn - Demo on Training k-nearest Neighbors For Our Model - Demo - Training a Random Forest