What is Decision Trees in Machine Learning

Introduction

In this blog, we would discuss what is Decision Trees in Machine Learning. Decision trees are one of the most popular machine learning algorithms. They are widely used in business, finance, and other fields because they provide a fast and easy way to make predictions. Decision trees are a type of supervised learning algorithm that uses the principle of splitting nodes and merging leaves. This can be used for classification, regression, clustering, and anomaly detection. Decision tree methods have their roots in statistics where they were first used to model continuous variables such as heights or weights. In modern machine learning applications they are often used for classification problems where you want to classify your data into categories (e.g., spam vs non-spam emails) or predict how likely it is that an event will happen (e.g., there will be sunny weather tomorrow).

What are Decision Trees?

According to a survey of KDnuggets, the decision tree algorithm is the most popular ML algorithm among data scientists.

Decision trees are an easy-to-understand algorithm for classification and regression. They are used in many machine learning applications, such as image recognition, text processing, and fraud detection.

Decision trees are a type of supervised learning algorithm that aims to predict the value of some target variable based on its categorical features (i.e., features with discrete values). The decision tree is constructed by splitting the original dataset into two parts: “decision nodes” or leaves and “branching nodes” which connect these leaves together through an internal path or parent node until we reach one leaf only (i.e., terminal node). Each branching node has a feature vector associated with it that uses all cases presently before this point in time (or after this point in time depending on how far back you go), but only includes those cases which actually fall under those categories defined by your model parameters at that particular time step

Evaluation Metrics 

Entropy

Entropy is a measure of uncertainty. It measures the amount of information that is missing, not just in the data (known), but also in how we interpret it (the interpretation).

Information Gain

Information gain measures how much information is gained by partitioning the data. It’s a measure of uncertainty, and it can be used to determine if an algorithm makes the right decision.

Conclusion

A decision tree algorithm is a powerful tool for making predictions on large datasets. It’s easy to implement and works well in practice, but it has limitations that require careful consideration before using it. If you are looking to use an algorithm with fewer parameters, try one of the more general methods like gradient descent or random forest instead.

Also, read – Implementation of Decision Trees in Python.