decision trees during training and outputs the mode of the classes (for classification)or the mean prediction (for regression) of the individual trees. Each tree in the forestis trained on a random subset of the training data and features, introducingrandomness into the learning process. This randomness helps to reduce overfittingand improve generalization performance. Random Forest is robust, scalable, andcapable of handling high-dimensional data with complex interactions betweenfeatures. It is widely used for classification tasks in various domains, includingfinance, healthcare, and marketing

What is a characteristic of Random Forests?Each tree is trained on a random subset of the featuresAll trees are identicalThey are sensitive to feature scalingThey are prone to overfitting

Which of the following is a technique used to reduce overfitting in the Random Forest algorithm?Review LaterDecreasing the number of estimatorsIncreasing the maximum depth of the decision treesIncreasing the subsample sizeIncreasing the learning rate

What is a decision tree mainly used for in machine learning?Review LaterData CleaningFeature SelectionData ClassificationData Aggregation

What is a Random Forest?Answer areaA type of neural networkAn ensemble learning method that uses multiple decision treesA clustering algorithmA linear model

decision tree and random forest with a real -time example