New answers tagged decision-trees
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XGBoost prints trees beyond n_estimator
It's because you're doing multiclass classification, and xgboost does that by building parallel models for each class. So the total number of trees is actually $512\cdot (\text{# classes})$.
I don't ...
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Decision tree vs logistic regression feature importances
The difference in the importance of the 'Total day charge' coefficient between the decision tree and logistic regression models is due to the way that each model ...
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Why do we need XGBoost and Random Forest?
First Question: XGBoost converts weak learners to strong learners. What's the advantage of doing this? Combining many weak learners instead of just using a single tree?
Just to get the vocabulary ...
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How does XGBoost compute the probabilities in predict_proba()?
XGBoost is a gradient-boosting algorithm, which means it builds an ensemble of weak decision trees in a sequential manner, where each tree learns to correct the mistakes of the previous trees. To ...
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Noisy Data Robustness - NN vs Decision Tree
It is not fair to say that Decision Trees are more sensitive to noise in the data comparing to NN.
It really depends on your model type (DT, NN, SVM, ... etc) and the model complexity. in general, ...
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