Random forests are one of the most commonly utilized supervised learning algorithms. While they can be used for both classification and regression tasks, we're going to focus on the former. Random forests are an example of an ensemble method, which works by aggregating the outputs of multiple models in order to construct a stronger performing model. Sometimes, you'll hear this being referred to as a grouping of weak learners to create a strong learner.
Setting up a random forest classifier in Python is quite simple with the help of scikit-learn. First, we import the modules and set up our data. We do not have to perform any data cleaning here, as the Iris dataset comes pre-cleaned.
Before training machine learning algorithms, ...
