Random Forest Classifier

Random Forest

The Random forest or Random Decision Forest is a supervised Machine learning algorithm used for classification, regression, and other tasks using decision trees. Random Forests are particularly well-suited for handling large and complex datasets, dealing with high-dimensional feature spaces, and providing insights into feature importance. This algorithm’s ability to maintain high predictive accuracy while minimizing overfitting makes it a popular choice across various domains, including finance, healthcare, and image analysis, among others....

Random Forest Classifier

The Random forest classifier creates a set of decision trees from a randomly selected subset of the training set. It is a set of decision trees (DT) from a randomly selected subset of the training set and then It collects the votes from different decision trees to decide the final prediction....

How Random Forest Classification works

Random Forest Classification is an ensemble learning technique designed to enhance the accuracy and robustness of classification tasks. The algorithm builds a multitude of decision trees during training and outputs the class that is the mode of the classification classes. Each decision tree in the random forest is constructed using a subset of the training data and a random subset of features introducing diversity among the trees, making the model more robust and less prone to overfitting....

Random Forest Classifier in Machine Learning

Step 1: Loading dataset...

Conclusion

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Frequently Asked Questions(FAQs)

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