What is a machine learning model?

Machine learning models are created from machine learning algorithms, which are trained using either labeled, unlabeled, or mixed data. Different machine learning algorithms are suited to different goals, such as classification or prediction modeling, so data scientists use different algorithms as the basis for different models.

How can a data scientist create a machine learning model?

A data scientist looking to create a machine learning model that identifies different animal species might train a decision tree algorithm with various animal images. Over time, the algorithm would become modified by the data and become increasingly better at classifying animal images. In turn, this would eventually become a machine learning model.

What is an example of a supervised machine learning model?

This is an example of an unsupervised machine learning model. Similarly, a mobile service provider might use machine learning to analyze user sentiment and curate its product offering according to market demand. This is an example of a supervised machine learning model. All machine learning models can be classified as supervised or unsupervised.

What are tree-based machine learning models?

Tree-based models are supervised machine learning algorithms that construct a tree-like structure to make predictions. They can be used for both classification and regression problems. In this section, we will explore two of the most commonly used tree-based machine learning models: decision trees and random forests.