query: Is it possible for decision trees to consider less features than in my training set?
positive_passages.docid: 151799
positive_passages.text: Decision tree not using all features from training dataset
negative_passages.docid: 101661
negative_passages.text: What should be the optimal parameters for Random Forest classifier?
negative_passages.docid: 6048
negative_passages.text: What should I do when my neural network doesn't learn?
negative_passages.docid: 118742
negative_passages.text: How is a splitting point chosen for continuous variables in decision trees?
negative_passages.docid: 156475
negative_passages.text: Validation Error less than training error?
negative_passages.docid: 121252
negative_passages.text: Interpreting output of importance of a random forest object in R
negative_passages.docid: 73022
negative_passages.text: Sklearn ValueError: X has 2 features per sample; expecting 11
negative_passages.docid: 88516
negative_passages.text: Is dimensionality reduction almost always useful for classification?
negative_passages.docid: 21979
negative_passages.text: Feature selection for "final" model when performing cross-validation in machine learning
negative_passages.docid: 112991
negative_passages.text: What algorithms need feature scaling, beside from SVM?
negative_passages.docid: 102449
negative_passages.text: Weighting features prior to SVM
negative_passages.docid: 110802
negative_passages.text: Model selection with low N?
negative_passages.docid: 104621
negative_passages.text: On the hardness of data to learn
negative_passages.docid: 50358
negative_passages.text: Choosing Random Forests' parameters
negative_passages.docid: 58515
negative_passages.text: dropout: forward prop VS back prop in machine learning Neural Network
negative_passages.docid: 47570
negative_passages.text: Will decision trees perform splitting of nodes by converting categorical values to numerical in practice?
negative_passages.docid: 42256
negative_passages.text: Binary classification with strongly unbalanced classes