Random Forest

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Table Of Contents

What Is Random Forest?

Random Forest (RF) refers to a machine learning (ML) algorithm employed for classification and regression tasks. It produces multiple decision trees during training and outputs the mode of the classes (during classification) or the mean prediction (during regression) of individual decision trees for better decision-making. Since it is trained on a set of already labeled data, it is a supervised ML algorithm.

The term random signifies the algorithm's approach of using a by-chance subset of features from the input data to create each tree. By-chance subsets help avoid overfitting, enhancing the accuracy and reliability of results. It is a widely used algorithm in finance, marketing, e-commerce, healthcare, and environmental science due to its effectiveness and adaptability.

What Is Random Forest?

Random forest is a machine learning model that generates diverse and random decision trees to derive robust and accurate predictions suitable for both classification and regression tasks.
This algorithm can be used in finance, marketing, investment, e-commerce, healthcare, and other fields for making predictions and decisions.
It differs from a decision tree, which is a single tree that makes decisions based on a set of rules.
It is a powerful extension of decision trees that addresses some of the limitations of individual decision trees, such as overfitting. It provides more accurate and reliable predictions, especially in complex datasets.

Random Forests Explained

Random Forest is an ensemble learning model that combines multiple individual decision trees to make predictions. Each tree independently anticipates the target variable, and the final projection or prediction is based on the comprehensive results delivered by the decision trees of all the datasets used for the task.

It starts by creating multiple subsets of the training dataset through a process called bootstrapping. This involves generating several random samples (with replacement) of the original dataset. Each of these subsets is used to train a separate decision tree. When building each decision tree, this model assumes a spontaneous subset of features at every split point. This random feature selection adds diversity to the forest.

Also, the number of features considered at each split is the square root of the total number of features. This is a hyperparameter that can be adjusted to improve the performance of the model. A hyperparameter is a parameter set before the training of a machine learning model that controls the learning process. Hyperparameters are not learned from the data but are defined by a researcher or analyst.

These decision trees are constructed based on the selected features and the bootstrapped samples. Moreover, the tree continues until each leaf node contains a small number of samples or a predefined maximum depth is reached.

For a random forest classifier, the mode or most frequent class of all the classes predicted by individual trees is taken as the final prediction. However, for random forest regression, the predictions from all trees are averaged to obtain the final prediction.

Moreover, by combining predictions from multiple trees and considering random subsets of data and features, this algorithm mitigates overfitting, a common issue with individual decision trees that tend to memorize the training data.

Also, during training, each tree is evaluated using the samples that were not included in its bootstrap sample, known as Out-of-Bag (OOB) data. The OOB error furnishes an assessment of the model's performance without requiring a separate validation dataset.

Examples

The random forest model is considered to be more efficient than the decision tree due to its analytical accuracy. Some real-life applications of this algorithm are discussed in this section.

Example #1

Suppose a digital marketing company wants to target viewers interested in watching a suspense thriller web series for an OTT channel. Hence, based on random forest algorithms, the company suggests related web series to users based on their preferences and online behavior.

Example #2

Suppose Ben is a stock analyst who uses a random forest model to devise and improve trading strategies. He predicts the possibility of stock price movements by applying various technical indicators to generate multiple random decision trees. Based on this, he identifies the market trend and invests accordingly.

Example #3

An August 2023 article discusses the ever-increasing dangers of natural hazards occurring around the world, with a particular focus on Indonesia's challenges related to droughts and forest fires.

It highlights their far-reaching impact on human lives, ecosystems, and the economy, underscoring the vulnerability of Indonesia's tropical forests. The study introduces a multi-hazard risk assessment using machine learning algorithms, explicitly highlighting the Random Forest (RF) model.

The research's objective is to develop precise risk maps for droughts and forest fires in Kalimantan Island, Indonesia, taking into account factors such as climate change. The methodology adopted for the study is outlined, including how hazard inventory data and the selection of non-hazardous locations for training and validating the machine learning models were collected.

Applications

Random Forest algorithm finds applications in various domains such as healthcare, finance, marketing, and e-commerce due to its versatility and robustness. Here are some common implications of this model:

Classification: Random Forest is widely used for classification tasks in areas such as spam email detection, sentiment analysis, and customer churn prediction.
Regression: It is employed in regression tasks like predicting sales figures, stock prices, or any numerical values based on multiple input features.
Credit Scoring: This model is used in credit scoring models to evaluate the creditworthiness of individuals, determining the likelihood of loan repayment.
Anomaly Detection: It helps in identifying anomalies or outliers in various fields, such as fraud detection in financial transactions or network security.
E-commerce: Random Forest algorithms are used in recommendation systems to suggest products, services, or content to users based on their choices and behavior.
Customer Relationship Management (CRM): It assists in customer segmentation and predicting customer behavior, helping businesses tailor their marketing strategies.
Healthcare: It aids in predicting diseases and medical conditions based on patient data, contributing to areas like cancer prediction and diagnosis.

Advantages And Disadvantages

The effectiveness of a random forest depends on the specific problem and data set. The following are the pros and cons of this popular machine learning algorithm.

Advantages	Disadvantages
Random forest generally provides highly accurate predictions by aggregating the outcomes of multiple decision trees.	Random forest models can become complex, making it challenging to interpret the results, especially when dealing with a large number of trees.
It is versatile since it can be used for both classification and regression tasks.	This model may not perform well with very small datasets. If the dataset is very small, practitioners will fall short of data while training decision trees, reducing the overall effectiveness.
Training individual decision trees in a random forest can be parallelized, making it suitable for large datasets.	Training a large number of trees can be computationally expensive and time-consuming.
It can efficiently handle and account for missing values. It uses two distinct methods for this called Imputation (using the most likely values to fill the gaps) and Proximity-based Averaging (using weighted averages to fill the gaps).	It is difficult to explain how a random forest model arrived at a particular prediction, which might prove to be a drawback in some applications. This happens because several individual decision trees are used simultaneously to arrive at a specific decision.
It mitigates overfitting by averaging the results from various decision trees.	These models require a considerable amount of memory, especially for storing multiple decision trees.
It is a robust method that performs well even if there are a large number of features and a small number of observations. Additionally, it ranks the features, improving such selection and data interpretation.	It can be biased towards features with more levels or categories. This happens because features with more categories or levels are able to offer more data points or information. It is called Categorical Feature Bias.

Random Forest vs Decision Tree

Random forests and decision trees are both machine-learning algorithms used for classification and regression tasks. The main difference between the two lies in their decision-making mechanisms. Some of these dissimilarities are discussed below.

Basis	Random Forest	Decision Tree
Meaning	It is an ensemble method that incorporates multiple decision trees to make forecasts.	It is a single tree-like structure where each internal node represents a feature, every branch symbolizes a decision rule, and each leaf node depicts the outcome.
Number of Decision Trees	It uses multiple random trees to reach an output.	A single decision tree helps reach a prediction or conclusion.
Prediction Process	Predictions are made by averaging or taking a majority vote from the predictions of individual trees in the forest.	Predictions are made based on the rules learned during training.
Decision-Making	Each tree in this model is trained on random data and features subsets for making predictions.	It makes decisions based on asking a series of questions about the input features. Each question leads to a new branch of the tree until a prediction is made at a leaf node. It is called Recursive Partitioning.
Feature Selection	It picks a random subset of features for each tree split. This random feature selection helps capture a more comprehensive range of patterns in the data and leads to more diverse trees.	Features are selected by their capacity to split the data effectively at each node. It might not consider all features, leading to biased results.
Overfitting	It reduces overfitting by averaging out the predictions of multiple trees, leading to more accurate and stable results compared to a single decision tree.	It is prone to overfitting, especially when the tree becomes deep and captures noise in the training data.
Training Time	It is a slow process.	This is faster than applying the RF method.
Interpretability	It is less interpretable due to the complexity of combining predictions from multiple trees.	It is easier to interpret and visualize.

Frequently Asked Questions (FAQs)

1. Is random forest supervised or unsupervised?

It is a supervised learning algorithm employed in machine learning that facilitates both classification and regression. It operates using labeled training data, learning from this information to make predictions effectively.

2. Is random forest an ensemble method?

Random forest is classified as an ensemble method due to its ability to combine predictions from multiple individual models, specifically decision trees. Unlike relying on a single decision tree, this model creates an ensemble of these trees, each trained on a different subset of data using bootstrapping.

3. Is random forest bagging or boosting?

Random forest uses the bagging algorithm, short for bootstrap aggregating. In bagging, the original dataset is divided into multiple subsets using bootstrapping, a sampling technique with replacement. Each subset aids in training a separate decision tree. These trees' predictions are then combined to make the final prediction.

4. Is random forest a neural network?

No, it is not the neural algorithm. Instead, it is an ensemble technique.