Contents
Does decision trees have feature selection?
Tree based models calculates feature importance for they need to keep the best performing features as close to the root of the tree. Constructing a decision tree involves calculating the best predictive feature. The feature importance in tree based models are calculated based on Gini Index, Entropy or Chi-Square value.
How do you decide a feature suitability when working with decision tree?
Answer: The steps associated with the deciding feature suitibility when working with decision trees are to make sure that prioritization is done properly. 1- When one or more complexity factors appear then two or more preprocessing techniques should be used. so step one should be to check for complexity of factors.
Is Random Forest a decision tree?
Random Forest is a tree-based machine learning algorithm that leverages the power of multiple decision trees for making decisions. Each node in the decision tree works on a random subset of features to calculate the output.
When does feature selection matter in a decision tree?
For ensembles of decision trees, feature selection is generally not that important. During the induction of decision trees, the optimal feature is selected to split the data based on metrics like information gain, so if you have some non-informative features, they simply won’t be selected.
How is the extra tree classifier for feature selection?
Each Decision Tree in the Extra Trees Forest is constructed from the original training sample. Then, at each test node, Each tree is provided with a random sample of k features from the feature-set from which each decision tree must select the best feature to split the data based on some mathematical criteria (typically the Gini Index).
How many decision trees are in a random forest?
Random forests consist of 4 –12 hundred decision trees, each of them built over a random extraction of the observations from the dataset and a random extraction of the features. Not every tree sees all the features or all the observations, and this guarantees that the trees are de-correlated and therefore less prone to over-fitting.
Can you split data in a decision tree?
As I know, splitting data in decision tree can use Gini Index or Entropy, but it can’t be used in feature selection. So how I set a threshold to split? Is it up to us to adjust the threshold? I read this journal but I still don’t understand Feature selection is just deciding which variable to include in your model.