What is the Random forest algorithm?

In a simple definition, What is the Random forest algorithm?

Random Forest is a supervised machine learning algorithm that is widely and comprehensively used in classification and regression problems. It builds decision trees on different samples and takes a majority vote for classification and the mean in the regression case.

How does the random forest algorithm work?

The term “Random Forest Classifier” refers to a classification algorithm made up of several multiple decision trees. A stochastic algorithm is used to build each tree individually to enhance non-correlated forests, which then uses predictive forest powers to make highly accurate decisions.

Why use the Random Forest algorithm

Here we can use the random forest algorithm for both classifications and regression tasks. Provides higher accuracy through validation. The random forest classifier will process the missing values and maintain the accuracy of a large percentage of the requested data.

Also, when we mention something about the Random Forest algorithm, one of the main advantages is that it reduces the risk of overfitting and the training time required. In addition, it offers a high level of accuracy. The Random Forest algorithm works efficiently in large databases and produces high-accuracy predictions by estimating missing data.

When should Random forests be used?

Here Random Forest is suitable for situations where we have a large data set, and interpretation is not a major concern. Decision trees are easier to interpret and understand. Because the random forest combines many decision trees, it becomes difficult to interpret

Some terms you need to know about the Random Forest algorithm:

Entropy

• It is a measure of randomness or unpredictability in the data set.

Information Gain

• A measure of the decrease in the entropy after the data set is split is the information gain.

Leaf Node

• A leaf node is a node that carries the classification or the decision.

Decision Node

• A node that has two or more branches.

Root Node

• The root node is the topmost decision node, which is where you have all of your data.

Show some of the Important Hyperparameters:

Here hyperparameters are used in the random forest either to increase the predictive power of the model or to make the model faster. Let’s look at the hyperparameters of learns built-in random forest function.

1. Increased predictive power

First of all, there are the hyperparameter n_estimators, which is just the number of trees the algorithm builds before taking the maximum vote or taking the averages of the predictions. In general, having more trees increases performance and makes predictions more stable and accurate, but it also slows down the computational process.

Another important hyperparameter is max_features, which is the maximum number of features a Random Forest group will see for a node split. Sklearn provides several options, all of which are described in the documentation.

The last important hyperparameter is min_sample_leaf. This specifies the minimum number of sheets required to split an internal node.

2. Increase the speed of the model

The n_jobs meta parameter tells the engine how many processors it is allowed to use. If its value is one, then it can use only one processor. The value “-1” means that there is no limit.

The random hyperparameter makes the form output reproducible. The model will always produce the same results when it has a specific value of random_state and if given the same hyperparameters and the same training data.

Finally, there is oob_score (also called oob sampling), which is a random forest validation method. In this sample, about a third of the data is not used to train the model and can be used to evaluate its performance. These samples are called out-of-bag samples. It is very similar to the one-out validation method, but almost no additional computational burden accompanies it.

Advantages and Disadvantages of the Random Forest Algorithm

One of the biggest advantages of Random Forest is its versatility. It can be used for both regression and classification tasks, and it’s also easy to view the relative importance it assigns to input features.

Random Forest is also a very useful algorithm because the default hyperparameters you use often lead to a good prediction result. Understanding hyperparameters is very easy, and there are not many of them either.

One of the biggest problems with machine learning is overfitting, but most of the time this won’t happen thanks to the random forest classifier. If there are enough trees in the forest, the classifier will not fit the model.

The main limitation of Random Forest is that a large number of trees can make the algorithm very slow and ineffective for real-time predictions. In general, these algorithms are quick to train, but they are very slow to generate predictions once they are trained. More accurate prediction requires more trees, which leads to a slower model. In most real-world applications, a random forest algorithm is fast enough but there can certainly be situations where runtime performance is important and other approaches are preferred.

Of course, Random Forest is a predictive modeling tool rather than a descriptive tool, which means if you’re looking for a description of the relationships in your data, other approaches are better.

Some possible causes in which random forests can be used:

The random forest intervenes in many areas, as it:

They are used in many different fields, such as banking, stock market, medicine, and e-commerce. In finance, for example, it is used to find out which customers are most likely to pay their debts on time, or who use the bank’s services frequently. In this field, it is also used to detect fraudsters to deceive the bank. In trading, the algorithm can be used to determine the future behavior of a stock. We can also say that it is used in health care, it is used to determine the correct combination of ingredients in medicine and to analyze a patient’s medical history to identify diseases. Random Forest is also used in e-commerce to determine whether a customer will actually like the product or not.

Summary of the above:

First of all, Random Forest is a very good algorithm for training early in the model development process, to see how it performs. Its simplicity makes building a “bad” random forest a difficult proposition.

An algorithm is also a great option for anyone who needs to quickly develop a model. Moreover, it provides a good indication of the importance it attaches to your features.

Random forests are also hard to beat in terms of performance. Of course, you can always find a model that can perform better, like a neural network for example, but it usually takes more time to develop, even though it can handle a lot of different types of features, such as binary, categorical, and scalar.

Overall, Random Forest is a fast, easy, and (mostly) flexible tool, but not without some limitations.

At the end of this article, it is considered that we have simplified and clarified the random forest in the following by defining it, its uses, why it is used, and so on. I hope that you have enjoyed and benefited from this simple article.

Mohamed B Mahmoud. Data Scientist.