Random Forest: Why It's One of the Best Machine Learning Algorithms
The Random Forest algorithm is considered one of the best machine learning algorithms, alongside Naïve Bayes and Neural Networks. Random Forest is essentially a collection of decision trees. It combines the individual trees into a single, more robust model. Typically, Random Forest is used for regression and classification problems with large datasets. Let’s learn more about Random Forest!
Understanding the Random Forest Algorithm Random Forest is a machine learning algorithm used for classifying large datasets. It works effectively across multiple dimensions and scales, offering high performance. Classification is done by combining multiple decision trees that are trained on subsets of your dataset.
As one of the top-performing algorithms in machine learning, Random Forest uses decision trees to carry out the selection process. Each decision tree is recursively split based on data from the same class. The more trees used in the forest, the more accurate the predictions tend to be. Classification results are determined by majority voting across all the trees.
How the Random Forest Algorithm Works Random Forest is a classification algorithm. So, how does it work? It builds several decision trees and merges them to get more stable and accurate predictions. The "forest" in Random Forest is essentially a collection of decision trees, typically trained using the bagging method. The general idea behind bagging is to combine multiple learning models to improve the overall result.
Random Forest increases randomness in the model while growing the trees. Instead of looking for the most important feature when splitting a node, Random Forest searches for the best feature among a random subset of features. This introduces more diversity and usually results in a better-performing model.
Random Forest Algorithm Implementation Random Forest is implemented across various sectors, from banking to e-commerce. Here are some real-world examples of Random Forest applications:
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Banking In banking, Random Forest is used to predict the creditworthiness of loan applicants. It helps lenders make the best decision on whether to approve a loan. It's also used to detect fraudulent activities.
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Healthcare In the healthcare sector, Random Forest is used for patient diagnosis. It analyzes previous medical records to help determine the correct medication dosage and treatment plans.
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Financial Analysis Financial analysts use Random Forest to identify potential stock markets. It also helps them assess the current condition of stocks.
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E-commerce E-commerce businesses use Random Forest in their daily operations. It helps predict customer preferences based on past buying behavior, enabling better product recommendations and personalized experiences.
example foto for random forest
source : https://algorit.ma/blog/cara-kerja-algoritma-random-forest-2022/