Random Forest (RF) is a simple algorithm widely used in data mining. Data mining is the process of extracting patterns, relationships, and useful insights from large datasets. There are two main types of data mining:
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Descriptive data mining: This method describes the existing data in detail.
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Predictive data mining: This method analyzes historical data to identify patterns that help make future predictions.
Two common data mining techniques in machine learning are classification and regression:
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Classification predicts a categorical variable. For example, a model diagnosing cancer disease patients into “healthy” or “sick.”
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Regression predicts a continuous variable. For instance, a model might estimate the age at which a high-risk patient could develop cancer. Instead of a category, the output is a number.
How does RF work?
Random Forest is a supervised learning algorithm, meaning it is trained on labeled data. The core of RF is the decision tree.
To build a Random Forest model:
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Multiple decision trees are created.
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Each tree is trained on random subsets of the data.
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Random features are selected for each tree.
The name Random Forest comes from this process of randomly selecting subsets of data to create multiple trees.
Bootstrap Aggregating
Bagging (Bootstrap Aggregating) is an essential concept of the Random Forest algorithm. It involves:
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Randomly selecting subsets of the dataset, allowing some samples to be chosen multiple times.
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Training multiple decision trees on these random subsets.
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Aggregating the predictions of all trees.
How Predictions Are Made?
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For classification models: Each tree votes for a category, and the most frequent category is chosen as the final prediction. For example, if most trees classify a patient as “sick,” the model predicts “sick.”
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For regression models: The average of all tree predictions is taken as the final output.
Random Forest is widely used because it improves accuracy, reduces overfitting, and works well with large datasets.