Understanding Random Forest Algorithm With Examples

Introduction

Random Forest is a widely-used machine learning algorithm developed by Leo Breiman and Adele Cutler, which combines the output of multiple decision trees to reach a single result. Its ease of use and flexibility, coupled with its effectiveness as a random forest classifier have, fueled its adoption, as it handles both classification and regression problems. In this article, we will understand how random forest algorithm works, and about its advantages , random forest regression techniques and how it differs from other algorithms and how to use it.

In this article, you will explore the Random Forest model, a powerful machine learning technique. We will provide a random forest numerical example to illustrate its functionality and effectiveness. You’ll learn what is random forest is and how it operates by aggregating predictions from multiple decision trees. Additionally, we will present a practical example to demonstrate its application in real-world scenarios. Finally, we will delve into analysis to uncover insights into variable importance and overall model performance.

Learning Objectives

• Learn the working of random forest with an example.
• Understand the impact of different hyperparameters.
• Implement them on a classification problem using scikit-learn.

This article was published as a part of the Data Science Blogathon.

Assumptions of Random Forest

To effectively use Random Forest, it is important to understand the underlying assumptions of the algorithm:

• Independence of Trees: The decision trees in the forest should be independent of each other. This is achieved through bootstrap sampling and feature randomness.
• Sufficient Data: Random Forest requires a large amount of data to build diverse trees and achieve optimal performance.
• Balanced Trees: The algorithm assumes that the individual trees are grown sufficiently deep to capture the underlying patterns in the data.
• Noisy Data Handling: Random Forest can handle noisy data, but it assumes that the noise is randomly distributed and not systematic.

What is Random forest?

Random forest, a popular machine learning algorithm developed by Leo Breiman and Adele Cutler, merges the outputs of numerous decision trees to produce a single outcome. Its popularity stems from its user-friendliness and versatility, making it suitable for both classification and regression tasks.

Its widespread popularity stems from its user-friendly nature and adaptability, enabling it to tackle both classification and regression problems effectively. The algorithm’s strength lies in its ability to handle complex datasets and mitigate overfitting, making it a valuable tool for various predictive tasks in machine learning.

One of the most important features of the Random Forest Algorithm is that it can handle the data set containing continuous variables, as in the case of regression, and categorical variables, as in the case of classification. It performs better for classification and regression tasks. In this tutorial, we will understand the working of random forest and implement random forest on a classification task.

Random Forest Applications

• Customer churn prediction: Businesses can use random forests to predict which customers are likely to churn (cancel their service) so that they can take steps to retain them. For example, a telecom company might use a random forest model to identify customers who are using their phone less frequently or who have a history of late payments.
• Fraud detection: Random forests can identify fraudulent transactions in real-time. For instance, a bank might employ a random forest model to spot transactions made from unusual locations or involving unusually large amounts of money.
• Stock price prediction: It can predict future stock prices. However, it is important to note that stock price prediction is a very difficult task, and no model is ever going to be perfectly accurate.
• Medical diagnosis: These can help doctors diagnose diseases. For example, a doctor might use a random forest model to help them diagnose a patient with cancer.
• Image recognition: It can recognize objects in images. For example, a self-driving car might use a random forest model to identify pedestrians and other vehicles on the road.

Real-Life Analogy of Random Forest

Let’s dive into a real-life analogy to understand this concept further. A student named X wants to choose a course after his 10+2, and he cant decide which course fit for his skill set. So he decides to consult various people like his cousins, teachers, parents, degree students, and working people. He asks them varied questions like why he should choose, job opportunities with that course, course fee, etc. Finally, after consulting various people about the course he decides to take the course suggested by most people.

Working of Random Forest Algorithm

Before understanding the working of the random forest algorithm in machine learning, we must look into the ensemble learning technique. Ensemble simplymeans combining multiple models. Thus a collection of models is used to make predictions rather than an individual model.

Ensemble uses two types of methods:

As mentioned earlier, Random forest Classifier works on the Bagging principle. Now let’s dive in and understand bagging in detail.

Bagging

Bagging, also known as Bootstrap Aggregation, serves as the ensemble technique in the Random Forest algorithm. Here are the steps involved in Bagging:

1. Selection of Subset: Bagging starts by choosing a random sample, or subset, from the entire dataset.
2. Bootstrap Sampling: Each model is then created from these samples, called Bootstrap Samples, which are taken from the original data with replacement. This process is known as row sampling.
3. Bootstrapping: The step of row sampling with replacement is referred to as bootstrapping.
4. Independent Model Training: Each model is trained independently on its corresponding Bootstrap Sample. This training process generates results for each model.
5. Majority Voting: The final output is determined by combining the results of all models through majority voting. The most commonly predicted outcome among the models is selected.
6. Aggregation: This step, which involves combining all the results and generating the final output based on majority voting, is known as aggregation.

Now let’s look at an example by breaking it down with the help of the following figure. Here the bootstrap sample is taken from actual data (Bootstrap sample 01, Bootstrap sample 02, and Bootstrap sample 03) with a replacement which means there is a high possibility that each sample won’t contain unique data. The model (Model 01, Model 02, and Model 03) obtained from this bootstrap sample is trained independently. Each model generates results as shown. Now the Happy emoji has a majority when compared to the Sad emoji. Thus based on majority voting final output is obtained as Happy emoji.

Boosting

Boosting is one of the techniques that use the concept of ensemble learning. A boosting algorithm combines multiple simple models (also known as weak learners or base estimators) to generate the final output. It is done by building a model by using weak models in series.

There are several boosting algorithms; AdaBoost was the first really successful boosting algorithm that was developed for the purpose of binary classification. AdaBoost is an abbreviation for Adaptive Boosting and is a prevalent boosting technique that combines multiple “weak classifiers” into a single “strong classifier.” There are Other Boosting techniques. For more, you can visit – 4 Boosting Algorithms You Should Know: GBM, XGBoost, LightGBM & CatBoost.

Steps Involved in Random Forest Algorithm

• Step 1: In this model, a subset of data points and a subset of features is selected for constructing each decision tree. Simply put, n random records and m features are taken from the data set having k number of records.
• Step 2: Individual decision trees are constructed for each sample.
• Step 3: Each decision tree will generate an output.
• Step 4: Final output is considered based on Majority Voting or Averaging for Classification and regression, respectively.

For example:

Consider the fruit basket as the data as shown in the figure below. Now n number of samples are taken from the fruit basket, and an individual decision tree is constructed for each sample. Each decision tree will generate an output, as shown in the figure. The final output is considered based on majority voting. In the below figure, you can see that the majority decision tree gives output as an apple when compared to a banana, so the final output is taken as an apple.

Important Features of Random Forest

• Random Forest is distinguished by several key features that contribute to its effectiveness and versatility:
• Diversity: Each decision tree in the Random Forest is built from a different subset of data and features. This diversity helps in reducing overfitting and improving the model’s generalization capability.
• Robustness: By averaging the results from multiple trees, Random Forest reduces the variance and improves the robustness of the predictions.
• Handling of Missing Values: It can handle missing values internally by using surrogate splits or by averaging results from other trees that do not have missing values for the same data points.
• Feature Importance: It provides insights into the importance of each feature in the prediction process. This can be particularly useful for feature selection and understanding the underlying data patterns.
• Scalability: Random Forest can be parallelized because each tree is built independently of the others. This makes it scalable to large datasets and high-dimensional data.
• Versatility: It can be used for both classification and regression tasks. The algorithm is also effective for tasks involving categorical and continuous variables.
• Stability: Due to the ensemble nature, It is less sensitive to changes in the training data compared to a single decision tree.
• Out-of-Bag Error Estimation: Random Forest provides an internal mechanism for estimating the model error without the need for a separate validation set. This is done using the out-of-bag (OOB) samples, which are not used in the construction of each tree.

Difference Between Decision Tree and Random Forest

Random forest is a collection of decision trees; still, there are a lot of differences in their behavior.

Thus random forests are much more successful than decision trees only if the trees are diverse and acceptable.

Important Hyperparameters in Random Forest

Hyperparameters are used in random forests to either enhance the performance and predictive power of models or to make the model faster.

Increase the Predictive Power

• n_estimators: Number of trees the algorithm builds before averaging the predictions.
• max_features: Maximum number of features random forest considers splitting a node.
• mini_sample_leaf: Determines the minimum number of leaves required to split an internal node.
• criterion: How to split the node in each tree? (Entropy/Gini impurity/Log Loss)
• max_leaf_nodes: Maximum leaf nodes in each tree

Increase the Speed

• n_jobs: it tells the engine how many processors it is allowed to use. If the value is 1, it can use only one processor, but if the value is -1, there is no limit.
• random_state: controls randomness of the sample. The model will always produce the same results if it has a definite value of random state and has been given the same hyperparameters and training data.
• oob_score: OOB means out of the bag. It is a random forest cross-validation method. In this, one-third of the sample is not used to train the data; instead used to evaluate its performance. These samples are called out-of-bag samples.

Coding in Python – Random Forest Classifier

Now let’s implement Random Forest in scikit-learn.

1. Let’s import the libraries.

# Importing the required libraries
import pandas as pd, numpy as np
import matplotlib.pyplot as plt, seaborn as sns
%matplotlib inline

2. Import the dataset.

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('heart_v2.csv')
print(df.head())
sns.countplot(df['heart disease'])
plt.title('Value counts of heart disease patients')
plt.show()

3. Putting Feature Variable to X and Target variable to y.

# Putting feature variable to X
X = df.drop('heart disease',axis=1)
# Putting response variable to y
y = df['heart disease']

4. Train-Test-Split is performed

# now lets split the data into train and test
from sklearn.model_selection import train_test_split

# Splitting the data into train and test
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, random_state=42)
X_train.shape, X_test.shape

5. Let’s import RandomForestClassifier and fit the data.

from sklearn.ensemble import RandomForestClassifier

classifier_rf = RandomForestClassifier(random_state=42, n_jobs=-1, max_depth=5,
                                       n_estimators=100, oob_score=True)

%%time
classifier_rf.fit(X_train, y_train)

# checking the oob score
classifier_rf.oob_score_

6. Let’s do hyperparameter tuning for Random Forest using GridSearchCV and fit the data.

rf = RandomForestClassifier(random_state=42, n_jobs=-1)

params = {
    'max_depth': [2,3,5,10,20],
    'min_samples_leaf': [5,10,20,50,100,200],
    'n_estimators': [10,25,30,50,100,200]
}

from sklearn.model_selection import GridSearchCV

# Instantiate the grid search model
grid_search = GridSearchCV(estimator=rf,
                           param_grid=params,
                           cv = 4,
                           n_jobs=-1, verbose=1, scoring="accuracy")

%%time
grid_search.fit(X_train, y_train)

grid_search.best_score_

rf_best = grid_search.best_estimator_
rf_best

From hyperparameter tuning, we can fetch the best estimator, as shown. The best set of parameters identified was max_depth=5, min_samples_leaf=10,n_estimators=10

7. Now, let’s visualize

from sklearn.tree import plot_tree
plt.figure(figsize=(80,40))
plot_tree(rf_best.estimators_[5], feature_names = X.columns,class_names=['Disease', "No Disease"],filled=True);

from sklearn.tree import plot_tree
plt.figure(figsize=(80,40))
plot_tree(rf_best.estimators_[7], feature_names = X.columns,class_names=['Disease', "No Disease"],filled=True);

The trees created by estimators_[5] and estimators_[7] are different. Thus we can say that each tree is independent of the other.

8. Now let’s sort the data with the help of feature importance

rf_best.feature_importances_

imp_df = pd.DataFrame({
    "Varname": X_train.columns,
    "Imp": rf_best.feature_importances_
})

imp_df.sort_values(by="Imp", ascending=False)

Advantages and Disadvantages of Random Forest Algorithm

Advantages

• It can be used in classification and regression problems.
• It solves the problem of overfitting as output is based on majority voting or averaging.
• It performs well even if the data contains null/missing values.
• Each decision tree created is independent of the other; thus, it shows the property of parallelization.
• It is highly stable as the average answers given by a large number of trees are taken.
• It maintains diversity as all the attributes are not considered while making each decision tree though it is not true in all cases.
• It is immune to the curse of dimensionality. Since each tree does not consider all the attributes, feature space is reduced.
• We don’t have to segregate data into train and test, as 30% of the data will always be unanalyzed by the decision tree made out of bootstrap.

Disadvantages

• Random forest is highly complex compared to decision trees, where decisions can be made by following the path of the tree.
• Training time is more than other models due to its complexity. Whenever it has to make a prediction, each decision tree has to generate output for the given input data.

Conclusion

Random forest is a great choice if anyone wants to build the model fast and efficiently, as one of the best things about the random forest Classifier is it can handle missing values. It is one of the best techniques with high performance, widely used in various industries for its efficiency. It can handle binary, continuous, and categorical data. Overall, random forest is a fast, simple, flexible, and robust model with some limitations.

Key Takeaways

• Random forest algorithm is an ensemble learning technique combining numerous classifiers to enhance a model’s performance.
• It is a supervised machine-learning algorithm made up of decision trees.
• It is used for both classification and regression problems.

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