Decision Tree Intuition. In Programming, we regularly use If-else conditions, even the Decision Tree working process is similar to an If-else condition.
Decision Trees are easy & Simple to implement & interpreted. Decision Tree is a diagram (flow) that is used to predict the course of action or a probability. Each branch of the decision tree represents an outcome or decision or a reaction. Decision Trees can be implemented in a variety of situations from personal to complex situations. The sequence of steps will give a better understanding easily.
In Programming, we regularly use If-else conditions, even the Decision Tree working process is similar to an If-else condition.
The below tree shows a simple implementation of different nodes in the decision tree.
Decision Tree is one of the most widely used machine learning algorithm. It is a supervised learning algorithm that can perform both classification and regression operations.
Decision Trees Classifier - Both of Regression Trees and Classification Trees are a part of CART (Classification And Regression Tree) Algorithm.
Maths behind Decision Tree Classifier. Before we see the python implementation of the decision tree. Let’s first understand the math behind the decision tree classification.
One of the most popular and used ML Algorithm. It’s one of the most simple and basic models of machine learning, which can be used both for classification and regression.
Machine Learning: Decision Trees. This blog covers another interesting machine learning algorithm called Decision Trees and it’s mathematical implementation.