Question 6This tree ensemble method only uses a subset of the features for each tree:1 pointStackingAdaboostBaggingRandom Forest

To answer this question, we need to understand the characteristics of each tree ensemble method listed in the options.

1. Stacking: This method uses multiple models to make predictions, combining them to improve accuracy. It generally does not restrict subsets of features for each model but rather trains models on the entire dataset.

2. Adaboost: This is a boosting method that adjusts the weights of samples based on the error of the previous predictions but does not specifically restrict the number of features used.

3. Bagging: Short for Bootstrap Aggregating, Bagging creates multiple versions of a dataset by resampling and trains a separate model for each. Each model generally uses the entire feature set.

4. Random Forest: This is an ensemble method specifically designed to use a subset of features. During the construction of each tree, Random Forest selects a random subset of features, making it robust against overfitting and ideal for handling high-dimensional data.

Based on this analysis, the tree ensemble method that only uses a subset of the features for each tree is:

Final Answer

Random Forest