Is feature selection necessary for decision trees?

Is feature selection necessary for decision trees?

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.

Does Random Forest require feature engineering?

Absolutely it is. Regardless of the machine learning algorithm you are using, the quality of your features is paramount. Random forests, or any other algorithm that I am aware of, cannot create this information without your intervention.

What is the process of feature engineering?

Feature engineering is the process of taking raw data and transforming it into features that can be used in machine learning algorithms. Features are the specific units of measurement that algorithms evaluate for correlations.

How are tree based algorithms implemented in Python?

Implementation of these tree based algorithms in R and Python Introduction to Tree Based Algorithms Tree based algorithms are considered to be one of the best and mostly used supervised learning methods. Tree based algorithms empower predictive models with high accuracy, stability and ease of interpretation.

Why are ensembles of tree-based models able to learn?

Laurae: This post is about decision tree ensembles (ex: Random Forests, Extremely Randomized Trees, Extreme Gradient Boosting…) and correlated features. It explains why an ensemble of tree models is able to learn when some features are (highly or perfectly) correlated.

Why are tree-based models robust to Correlated features?

Just sharing my thought as I am using a tree-based model. Tree-based models have an innate feature of being robust to correlated features. When you drop a correlated variable to others, it will leave room for the tree to use one more variable in its trees.

How are decision trees used in feature engineering?

Decision Trees do not have an inherent way of summing up different features. They would need to create one leaf for every possible combination to synthesize Count features well. Also, Neural Net and SVM perform worse with ratio features. At last, the results confirm that Decision Trees can synthesize monotonic transformation features.