query: Tree complexity in boosted regression trees
positive_passages.docid: 156014
positive_passages.text: Tree complexity using gbm
negative_passages.docid: 1989
negative_passages.text: What is the VC dimension of a decision tree?
negative_passages.docid: 38739
negative_passages.text: Restrictions on universal generalization
negative_passages.docid: 27266
negative_passages.text: XGBoost Loss function Approximation With Taylor Expansion
negative_passages.docid: 108828
negative_passages.text: Kernel Ridge Regression Efficiency
negative_passages.docid: 136197
negative_passages.text: Normalizing the final weights vector in the upper bound on the Perceptron's convergence
negative_passages.docid: 69637
negative_passages.text: Random Forests and data transformations
negative_passages.docid: 39736
negative_passages.text: Regularization methods for logistic regression
negative_passages.docid: 123307
negative_passages.text: Ideas for outputting a prediction equation for Random Forests
negative_passages.docid: 131954
negative_passages.text: Choosing IRLS over gradient descent in logistic regression
negative_passages.docid: 40520
negative_passages.text: In boosting, why are the learners "weak"?
negative_passages.docid: 44881
negative_passages.text: Using lasso for feature selection, followed by a non-regularized regression
negative_passages.docid: 81133
negative_passages.text: Adaboost vs Gradient Boosting
negative_passages.docid: 108556
negative_passages.text: Standardization vs. Normalization for Lasso/Ridge Regression
negative_passages.docid: 69831
negative_passages.text: Obtaining knowledge from a random forest
negative_passages.docid: 139415
negative_passages.text: Interpretation of Importance score in Random Forest
negative_passages.docid: 101796
negative_passages.text: Understanding complete separation for logistic regression