Which statement is Not true about Gradient Boosted Trees? Group of answer choices Boosting methods are prone to overfitting. As the number of iterations/trees is increased, the gradient boosted tree method may fit an overly complex function to the training data and hence, overfit. The hyper-parameter is a shrinkage factor which controls such overfitting. It is an additive model Each subsequent tree uses the residuals left from the previous trees as outcome variable and is fit to these residuals. The residuals are updated at each iteration It is better to fit a deep tree instead of a shallow tree in each iteration of Gradient boosting to avoid overfitting
Which statement is Not true about Gradient Boosted Trees? Group of answer choices Boosting methods are prone to overfitting. As the number of iterations/trees is increased, the gradient boosted tree method may fit an overly complex function to the training data and hence, overfit. The hyper-parameter is a shrinkage factor which controls such overfitting. It is an additive model Each subsequent tree uses the residuals left from the previous trees as outcome variable and is fit to these residuals. The residuals are updated at each iteration It is better to fit a deep tree instead of a shallow tree in each iteration of Gradient boosting to avoid overfitting
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Which statement is Not true about Gradient Boosted Trees?
Group of answer choices
Boosting methods are prone to overfitting. As the number of iterations/trees is increased, the gradient boosted tree method may fit an overly complex function to the training data and hence, overfit. The hyper-parameter is a shrinkage factor which controls such overfitting.
It is an additive model
Each subsequent tree uses the residuals left from the previous trees as outcome variable and is fit to these residuals.
The residuals are updated at each iteration
It is better to fit a deep tree instead of a shallow tree in each iteration of Gradient boosting to avoid overfitting
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