Does random forest work if there are very few features?
I read doc and know tree-based model is determined by entropy or gini impurity. When model try to create leaf nodes, it will determine based on the feature, right? Ex: I have 2 features A,B, and I divide it with A. So I have left and right nodes based on A. It should have the best shape if I create nodes based on A, right? Now if I have 100 estimators but I only have two features, do I have different trees which are all based on feature A? or the shape of trees based on A are all the same cuz they were created by feature A? thx
Hi everyone, I stumbled upon this reddit thread [1] where people point out what they dislike about the scikit-learn API. It's mostly about the lack of consistency for linear models. Just thought it'd be interesting to have some external critics. Best, Nicolas [1] https://www.reddit.com/r/MachineLearning/comments/aryjif/d_alternatives_to_s...
I agree with most of their points and have tried to prioritize some (and I think you were the victim of me trying to address some of these ;). The question about structuring the estimators is really something tricky. Maybe it's worth putting it on the roadmap to discuss this at some point? Generally I thought it would be too much a hassle but the inconsistency is kind of annoying (having a class per loss or per regularizer or per solver sometimes). On 2/19/19 5:56 AM, Nicolas Hug wrote:
Hi everyone,
I stumbled upon this reddit thread [1] where people point out what they dislike about the scikit-learn API. It's mostly about the lack of consistency for linear models. Just thought it'd be interesting to have some external critics.
Best,
Nicolas
[1] https://www.reddit.com/r/MachineLearning/comments/aryjif/d_alternatives_to_s...
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lampahome -
Nicolas Hug