Who Needs Random Forest Machine Learning

If you’re not in the data science field or a tech enthusiast, you might not ever have heard of machine learning (ML), much less understand what it is. However, data science is one of the fastest-growing career fields in the United States. So, if you like computer science, ML is a career path that could lead to great prosperity.

The data science field is expanding so quickly because it’s a field of computer science that’s penetrated every sector of society. Machine learning is one of the most exciting and promising areas of data science and is already impacting our lives in meaningful and fun ways. The random forest ML model is one of the most popular and for many great reasons. In this article, we’ll discuss why random forest is so popular and some of the fields of enterprise in which it’s most prominent.

What is the random forest?

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The first step to understanding how crucial a role the random forest plays in our everyday lives is learning what it is. The random forest machine learning model is an algorithm used to make predictions. An algorithm is an equation machines use to make decisions based on data and patterns.

The random forest is a collection of decision trees. A decision tree is an ML model that consists of three different types of nodes. These include the root node, which is the original problem; decision nodes, which are different variables; and leaf nodes, which are the nodes machines use to make predictions. The number of trees in a random forest model ranges from 64 to 128. 

What are the benefits of the random forest algorithm?

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Of course, the random forest is so popular, it has to have some benefits, right? One of the best things about it is its accuracy. It’s far more accurate than a decision tree, and it can make predictions without the labor-intensive hyperparameter tuning process. 

Another great thing about this model is its versatility. This algorithm is capable of regression and classification problems, increasing its use cases.

One of the best features of the random forest model is that it’s easy to learn compared to many other algorithms. Its simplicity makes it a great learning tool for people interested in learning more about machine learning. In addition, its ease of use makes it perfect for professionals in many fields of endeavor, some of which we’ll cover in the next section.

What are some use cases of the random forest algorithm?

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Until something can be put into practice, it’s merely a concept. One sector in which the random forest algorithm is a favorite is the stock market. Stockbrokers can use it to predict the behavior of stocks and the reactions of people to the market.

Lenders use the random forest to make important decisions, such as which people are good loan candidates. This algorithm enables them to go deeper than income, banking statements, and even credit scores by combining insights from all three and putting them through a machine learning algorithm.

Retailers also use the random forest model. This algorithm helps them identify patterns in product demand and make product suggestions based on customer behavior, enabling them to build better connections with customers.

There are plenty of great reasons to learn, implement, and love the random forest. First, it’s a versatile algorithm capable of regression and classification problems, making it more accurate than a decision tree. Furthermore, it doesn’t require hyperparameter tuning, which is labor-intensive and time-consuming. Not to mention, it solves many of the problems that other algorithms present. It even solves the problem of overfitting so common when machine learning models learn the training set too well. That’s why it’s used in every field of endeavor, from ride-sharing to healthcare.

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