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

14. A Junior analyst trained an Sklearn random forest classifier to predict the winner of football matches. The model is performing well on the train data set. When the model was evaluated on the test dataset it performed poorly. Which of the following will be useful in solving this problem?. Select all correct.increasing the min samples leaf in each tree of the forestreducing the number of trees in the forestdecreasing the max depth of each tree in the forestreducing the min samples split in each tree of the forest

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

Which of the following is NOT a typical method to improve an overfitting machine learning model?Add more dataSelect more featuresSelect a simpler algorithmImprove feature engineering

Question 5Which of the following describes a way to regularize a decision tree to address overfitting?1 pointIncrease the max depth.Decrease the max depth.Increase the number of branches.Reduce the information gain.

Which technique can help reduce overfitting in machine learning models? Increasing model complexity Decreasing the amount of training data Regularization Ignoring feature importance