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Random Forest Algorithm
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Random Forest is an ensemble learning algorithm that builds multiple decision trees and merges them together to get a more accurate and stable prediction. It is used for both classification and regression tasks and improves over individual decision trees by reducing overfitting and improving predictive performance. The core idea behind a random forest is to combine the predictions of several independent decision trees, each built on a random subset of the data and features, and use a majority vote (in classification) or an average prediction (in regression) to make the final prediction. • Random forest is a highly versatile algorithm used in a wide range of applications. Its ensemble nature, which reduces overfitting and increases accuracy, makes it one of the most powerful and reliable machine learning algorithms for both classification and regression tasks. It’s especially effective in handling large datasets and high-dimensional feature spaces while maintaining interpretability and robustness. • Don't forget to like, share and subscribe for more videos.. • Dr.Irshad Ahmed • https://sites.google.com/view/drirsha... • / @drirshadahmed-rk5mr
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