The typical approach to remedy this is by pruning the tree to prevent overfitting. As you can imagine this process is prone to overfitting as it can result in a very deep tree with many nodes. We can repeat the splitting process iteratively at each child node until it is impossible to split further. There are various different approaches to split the tree, another common one is by minimizing the Gini Impurity. This algorithm will begin at the root node (the topmost node - in our case Is the Current Laptop Broken) and split the data on the feature which results in the largest information gain. This same concept can be used for non categorical data like integers. This is a classification example where buy and don't buy are our given labels.ĭecision tree classifier example on determining whether a user should purchase a new laptop or not (Image provided by author)īased on the features we train the tree on, the model will learn a series of questions to infer the class labels of the given data. We can outline this in the image below, in the following example our given problem is to determine whether or not an individual should purchase a new laptop. Imagine a directed, acyclic graph where each node is represented as a decision and the edges connect a pair of decisions. Just as the name suggests, decision tree is a type of model which breaks down the given input data through decisions based on asking a series of questions. Random Forest - Implementation - Advantages - Disadvantagesĭecision Trees are attractive models for businesses if their focus is on interpretability of results.Decision Tree - Implementation - Advantages - Disadvantages.Below I’ve outlined the structure this article will follow. I will also show the reader on how to implement both the random forest and decision tree algorithms in Python using the sklearn library on the iris dataset for flower classification. You can apply a similar thought process described below for regression based problems, but these algorithms will be out performed by other algorithms (like logistic regression) which specifically focus on those tasks. Although this algorithm is robust enough for both classification and regression based problems, this article will focus on the classification based examples. This article will provide a conceptual understanding of the decision tree and random forest algorithms. One of the strongest attributes to this algorithm is that it allows users to see which features contribute the most to the prediction and its importance based on the depth of the tree. Decision Trees and Random Forests are robust algorithms commonly used in the industry because of their ease of interpretability and performance.
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