Understanding Random Forest Machine Learning

Understanding Random Forest Machine Learning

Machine learning has become an integral part of business and society over the past couple of decades. It would take more time than you have to read this article to tell you all the ways machine learning (ML) impacts our lives, from entertainment and online shopping to predicting the effects of climate change. You may think you know ML, but you have no idea.

Machine learning uses algorithms to make predictions that impact customer experience and public services. They employ mathematic equations on data sets and classify what they learn based on these equations. Random forest is one of the rockstars of algorithms, and we’ll explain why.

A random forest is a bunch of decision trees.

When you think of a forest, you probably think of a bunch of trees. Well, it’s the same thing with the random forest machine learning algorithm. It’s a collection of decision trees that make up a forest. The number of trees in a random forest ranges between 64 and 128 decision trees.

The best way to understand the random forest is to understand a decision tree. Decision trees consist of root nodes, decision nodes, and leaf nodes. Root nodes represent the original problem, and each decision node represents possible outcomes that lead to another equation. The decision trees branches stop expanding when decision nodes become decision leaves. These leaves are attributes that predict outcomes. 

The random forest has many great features.

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There are many great advantages to the random forest model, which is why it’s one of the most popular algorithms. One of the features of random forest is that it solves the problem of overfitting. Overfitting is when an ML algorithm learns a subset of data so well that it learns the detail and noise of the data and picks them up in training sets and understands those concepts as models. It can lead to inaccurate predictions and other problems.

Another feature of this ML model is it chooses a random subset of features at each node on the tree. That means it solves the problem of missing values, providing high accuracy of the final output. Its simplicity and predictive accuracy are among its best features.

Another great thing about the random forest is it’s great at classification and regression problems. Additionally, unlike most other algorithms, it can even make accurate parameters without the laborious process of hyperparameter tuning. This versatility makes it an optimal algorithm for plenty of use cases, some of which we’ll discuss in the last section. 

There are many use cases for the random forest algorithm.

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As mentioned previously, the random forest model is a favorite algorithm of data scientists for many reasons. It’s simple to learn, implementation is fast, and the algorithm randomly adds subsets at every node until reaching the target variable—the list goes on. 

If data scientists love this algorithm, it stands to reason that the people who consult them love it, too. E-commerce is one of the greatest uses for this machine learning model. Merchants can use it to learn consumer behavior and make product suggestions and targeted promotions. Banks use the random forest algorithm to make decisions about loans. Stockbrokers even use it to predict the stock market, so you know it’s powerful.

The random forest algorithm is one of the most popular machine learning algorithms for a reason. It’s simple and effective for regression and classification problems. Moreover, there are plenty of use cases for this algorithm in some of the products and services we use and value most. Some of its most significant benefits are that it can accurately predict outcomes, employs the majority vote and cross-validation for decision-making, and produces more accurate results with the input of more datasets. If you’re planning on going into the machine learning field of data science, you should familiarize yourself with the random forest algorithm.

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