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Random Forest I

Random Forest is a supervised machine learning algorithm used for both classification and regression tasks, leveraging ensemble learning by combining multiple decision trees. It improves predictive accuracy by averaging the predictions from various trees based on majority votes. A larger number of trees enhances accuracy and mitigates overfitting issues.

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3 views1 page

Random Forest I

Random Forest is a supervised machine learning algorithm used for both classification and regression tasks, leveraging ensemble learning by combining multiple decision trees. It improves predictive accuracy by averaging the predictions from various trees based on majority votes. A larger number of trees enhances accuracy and mitigates overfitting issues.

Uploaded by

getu zerga
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Random Forest is a popular machine learning algorithm that belongs to the supervised learning

technique. It can be used for both Classification and Regression problems in ML. It is based on the
concept of ensemble learning, which is a process of combining multiple classifiers to solve a complex
problem and to improve the performance of the model.

As the name suggests, "Random Forest is a classifier that contains a number of decision trees on various
subsets of the given dataset and takes the average to improve the predictive accuracy of that dataset."
Instead of relying on one decision tree, the random forest takes the prediction from each tree and based
on the majority votes of predictions, and it predicts the final output.

The greater number of trees in the forest leads to higher accuracy and prevents the problem of
overfitting.

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