Random Forest Algorithm: When to Use & How to Use? [With Pros & Cons]
Updated on Sep 23, 2022 | 7 min read | 6.8k views
Share:
For working professionals
For fresh graduates
More
Updated on Sep 23, 2022 | 7 min read | 6.8k views
Share:
Table of Contents
Data Science encompasses a wide range of algorithms capable of solving problems related to classification. Random forest is usually present at the top of the classification hierarchy. Other algorithms include- Support vector machine, Naive Bias classifier, and Decision Trees.
Before learning about the Random forest algorithm, let’s first understand the basic working of Decision trees and how they can be combined to form a Random Forest.
Enrol for the Machine Learning Course from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career.
Decision Tree algorithm falls under the category of Supervised learning algorithms. The goal of a decision tree is to predict the class or the value of the target variable based on the rules developed during the training process. Beginning from the root of the tree we compare the value of the root attribute with the data point we wish to classify and on the basis of comparison we jump to the next node.
Moving on, let’s discuss some of the important terms and their significance in dealing with decision trees.
Read: Decision Tree Interview Questions
Decision Tree algorithm is prone to overfitting i.e high accuracy on training data and poor performance on the test data. Two popular methods of preventing overfitting of data are Pruning and Random forest. Pruning refers to a reduction of tree size without affecting the overall accuracy of the tree.
Now let’s discuss the Random forest algorithm.
One major advantage of random forest is its ability to be used both in classification as well as in regression problems.
As its name suggests, a forest is formed by combining several trees. Similarly, a random forest algorithm combines several machine learning algorithms (Decision trees) to obtain better accuracy. This is also called Ensemble learning. Here low correlation between the models helps generate better accuracy than any of the individual predictions. Even if some trees generate false predictions a majority of them will produce true predictions therefore the overall accuracy of the model increases.
Random forest algorithms can be implemented in both python and R like other machine learning algorithms.
First of all, we need to decide whether the problem is linear or nonlinear. Then, If the problem is linear, we should use Simple Linear Regression in case only a single feature is present, and if we have multiple features we should go with Multiple Linear Regression. However, If the problem is non-linear, we should Polynomial Regression, SVR, Decision Tree, or Random
Forest. Then using very relevant techniques that evaluate the model’s performance such as k-Fold Cross-Validation, Grid Search, or XGBoost we can conclude the right model that solves our problem.
For any beginner, I would advise determining the number of trees required by experimenting. It usually takes less time than actually using techniques to figure out the best value by tweaking and tuning your model. By experimenting with several values of hyperparameters such as the number of trees. Nevertheless, techniques like cover k-Fold Cross-Validation and Grid Search can be used, which are powerful methods to determine the optimal value of a hyperparameter, like here the number of trees.
Here, the p-value will be insignificant in the case of Random forest as they are non-linear models.
Decision trees are highly sensitive to the data they are trained on therefore are prone to Overfitting. However, Random forest leverages this issue and allows each tree to randomly sample from the dataset to obtain different tree structures. This process is known as Bagging.
Bagging does not mean creating a subset of the training data. It simply means that we are still feeding the tree with training data but with size N. Instead of the original data, we take a sample of size N (N data points) with replacement.
Random forest algorithms allow us to determine the importance of a given feature and its impact on the prediction. It computes the score for each feature after training and scales them in a manner that summing them adds to one. This gives us an idea of which feature to drop as they do not affect the entire prediction process. With lesser features, the model will less likely fall prey to overfitting.
The use of hyperparameters either increases the predictive capability of the model or make the model faster.
To begin with, the n_estimator parameter is the number of trees the algorithm builds before taking the average prediction. A high value of n_estimator means increased performance with high prediction. However, its high value also reduces the computational time of the model.
Another hyperparameter is max_features, which is the total number of features the model considers before splitting into subsequent nodes.
Further, min_sample_leaf is the minimum number of leaves required to split the internal node.
Lastly, random_state is used to produce a fixed output when a definite value of random_state is chosen along with the same hyperparameters and the training data.
Also Read: Types of Classification Algorithm
Random forest algorithm is a very powerful algorithm with high accuracy. Its real-life application in fields of investment banking, stock market, and e-commerce websites makes them a very powerful algorithm to use. However, better performance can be achieved by using neural network algorithms but these algorithms, at times, tend to get complex and take more time to develop.
If you’re interested to learn more about the decision tree, Machine Learning, check out IIIT-B & upGrad’s PG Diploma in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.
Get Free Consultation
By submitting, I accept the T&C and
Privacy Policy
Top Resources