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Top resources to learn decision trees in 2022


Decision trees are a supervised learning method used to build a model that predicts the value of a target variable by learning simple decision rules from the data features. DTs are used for both classification and regression and are simple to understand and interpret. Below, we have listed down the top online courses, YouTube videos and guides for enthusiasts to master decision trees. The course by CodeAcademy focuses on teaching developers how to build and use decision trees and random forests. The course looks at two methods in detail: Gini impurity and Information Gain.

Fundamentals of Decision Trees in Machine Learning


A tree has many analogies in real life, and turns out that it has influenced a wide area of machine learning, covering both classification and regression. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. If you're working towards an understanding of machine learning, it's important to know how to work with decision trees. This course covers the essentials of machine learning, including predictive analytics and working with decision trees. In this course, we'll explore several popular tree algorithms and learn how to use reverse engineering to identify specific variables.

Logistic Regression, Decision Tree and Neural Network in R


In this course, we cover two analytics techniques: Descriptive statistics and Predictive analytics. For the predictive analytic, our main focus is the implementation of a logistic regression model a Decision tree and neural network. We well also see how to interpret our result, compute the prediction accuracy rate, then construct a confusion matrix .