Computer Science Homework Help. Harvard University Machine Learning Stacking & Dataset Questions
1)A random forest grows a forest of many trees…
How is each tree different from one another?
2) What is the difference between 10-Fold CV and a bagging exercise of 10 bootstrapped
samples in the way we run the experiments?
for Q3, Q4 AND Q5, CONSIDER THE following ensemble strategies:
bagging, boosting, randomforest, stacking, cross-validation.
3) If parallelizability is an important factor, which of the following methods
will you not consider:
WHY?
4)You are limited by size of the dataset (small dataset) which of the ensemble methods will
you not consider?
WHY?
5) What problem does Stacking overcome that all other strategies fail to overcome?
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These questions are designed to evaluate my understanding and comprehension of mathematics, datasets and process of machine learning. I am encouraged to source information from anywhere by doing any outside research as long as credit is given. Please give logical development and reasoned basis for the conclusion. No exact word count or length requirement– enough to answer the questions, but I think over-explaining is better and I imagine at least 200 words for each. Does that sound right to you?
