The Random Forest Algorithm: Its Working, Applications, and Example

[Photo by Kyle Glenn on Unsplash]

Random forest is a powerful machine learning algorithm that has become increasingly popular in recent years. It is a type of ensemble learning algorithm that combines multiple decision trees to create a more robust and accurate model. Random forest has proven to be a versatile algorithm, with applications ranging from classification and regression to anomaly detection and feature selection. In this article, we will delve into the inner workings of random forest, its applications, and its ability to perform feature selection.

How does random forest work?

At a high level, random forest works by building multiple decision trees on randomly sampled subsets of the training data and combining their predictions to make the final prediction. Each decision tree in the random forest is built using a different random subset of the training data and a random subset of the features. This helps to reduce over-fitting and improve the model’s generalization ability.

Here are the steps involved in building a random forest:

1. Randomly sample the training data with replacement to create multiple bootstrap samples.
2. For each bootstrap sample, randomly select a subset of the features to use for building the decision tree.
3. Build a decision tree using the selected features and bootstrap sample.
4. Repeat steps 2 and 3 for a predetermined number of trees.
5. When making a prediction for a new data point, pass it through all the decision trees in the forest and combine their predictions to make the final prediction.

By building multiple decision trees and combining their predictions, random forest can achieve higher accuracy than a single decision tree. Additionally, the random sampling of the training data and features helps to reduce the variance and over-fitting of the model.

Applications of random forest

Random forest is a versatile algorithm that can be applied to a wide range of problems. Here are some of the most common applications of random forest:

1. Classification: Random forest can be used for classification problems, where the goal is to predict the class of a new data point. It has been used in a variety of applications such as fraud detection, customer churn prediction, and image classification.
2. Regression: Random forest can also be used for regression problems, where the goal is to predict a continuous output variable. It has been used in applications such as predicting housing prices and stock prices.
3. Anomaly detection: Random forest can be used for anomaly detection, where the goal is to identify data points that are significantly different from the rest of the data. It has been used in applications such as credit card fraud detection and network intrusion detection.
4. Feature selection: Random forest can also be used for feature selection, where the goal is to identify the most important features for predicting the output variable. This can help to reduce the dimensionality of the data and improve the performance of other machine learning algorithms.

Building a Random Forest Classifier

A random forest classifier can be implemented using Scikit Learn in Python by following the steps below:

Step-1: Importing the necessary libraries

In this case, we will be using Scikit-learn, so we need to import it as well as other libraries such as numpy and pandas. Here is an example of how to import these libraries:

Figure 1. Importing Libraries

Step-2: Loading the data

This could be in the form of a CSV file or some other format. For this example, we will be using the Iris dataset, which is available in Scikit-learn. Here is an example of how to load the data:

Figure 2. Importing Dataset

In this code, we load the Iris dataset, which contains 150 samples of iris flowers, each with 4 features (sepal length, sepal width, petal length, and petal width). The target variable contains the class labels (0, 1, or 2) corresponding to the 3 species of iris flowers.

Step-3: Splitting the data into training set and testing set

Before training the classifier, you need to split the data into training and testing sets. This is important to evaluate the performance of the classifier on unseen data. Here is an example of how to split the data:

Figure 3. Splitting the data

In this code, we use the train_test_split function to split the data into 70% for training and 30% for testing. The random_state parameter ensures that the split is reproducible.

Step-4: Training the classifier

Now that the data is split, you can train the random forest classifier. Here is an example of how to do this:

Figure 4. Training the classifier

In this code, we create a random forest classifier with 100 trees and use the fit method to train it on the training data.

Step-5: Evaluating the performance of the classifier

Once the classifier is trained, you need to evaluate its performance on the testing data. Here is an example of how to do this:

Figure 5. Evaluating the classifier

In this code, we use the predict method to predict the class labels for the testing data, and then use the accuracy_score function to calculate the accuracy of the predictions.

Step-6: Hyper-parameter Tuning

Random forest classifiers have several hyper-parameters that can be tuned to improve their performance. For example, the number of trees, the maximum depth of each tree, and the minimum number of samples required to split a node. You can use techniques such as cross-validation or grid search to find the optimal values for these hyper-parameters. Here is an example of how to do this:

Figure 6. Hyper-parameter Tuning of Random Forest Classifier