Contents
What is a boosted tree model?
Boosting means combining a learning algorithm in series to achieve a strong learner from many sequentially connected weak learners. In case of gradient boosted decision trees algorithm, the weak learners are decision trees. Each tree attempts to minimize the errors of previous tree.
How does a decision stump work?
A decision stump makes a prediction based on the value of just a single input feature. Sometimes they are also called 1-rules. For continuous features, usually, some threshold feature value is selected, and the stump contains two leaves — for values below and above the threshold.
Is xgboost faster than random forest?
For most reasonable cases, xgboost will be significantly slower than a properly parallelized random forest. If you’re new to machine learning, I would suggest understanding the basics of decision trees before you try to start understanding boosting or bagging.
How are decision trees different from other trees?
A decision tree will be able to represent that concept, in exponential size. Any of the features alone is useless (or misleading). A decision stump is restricted to a single feature. Even if the decision stumps will choose the right features, one cannot represent XOR using a linear weight of the features .
How are boosted decision stumps different from machine learning?
For example, taking the image from sebastian raschkas post “Machine Learning FAQ”: I would expect a very similar (if not exactly the same) result for a decision tree: Given only two features, it finds the optimal feature (and value for that feature) to split the classes.
How to make the sklearn decisiontreeclassifier a weak classifier?
To make the sklearn DecisionTreeClassifier a *weak* classifier we will set *max_depth* parameter == 1 to create something called a *decision stump* which is in principal (as stated above) nothing else as a decision tree with only one layer, that is, the root node directly ends in leaf nodes and hence the dataset is split only once.
Which is more accurate boosted trees or random forests?
The accuracy of boosted trees turned out to be equivalent to Random Forests with respect and even often outperforms the latter (see for instance Caruana and Niculescu-Mizil (2008) (*An Empirical Comparison of Supervised Learning Algorithms*)).