What is feature selection RFE?

RFE is a wrapper-type feature selection algorithm. This means that a different machine learning algorithm is given and used in the core of the method, is wrapped by RFE, and used to help select features. This process is repeated until a specified number of features remains.

Why is feature selection important?

Top reasons to use feature selection are: It enables the machine learning algorithm to train faster. It reduces the complexity of a model and makes it easier to interpret. It improves the accuracy of a model if the right subset is chosen.

What is the difference between feature selection and feature extraction?

Feature Selection. The key difference between feature selection and extraction is that feature selection keeps a subset of the original features while feature extraction creates brand new ones.

How does Python implement feature selection?

4 ways to implement feature selection in Python for machine…

1. Univariate selection.
2. Recursive Feature Elimination (RFE)
3. Principle Component Analysis (PCA)
4. Choosing important features (feature importance)

Which feature selection technique used in recursive approach?

Recursive feature elimination
Recursive feature elimination (RFE) is a feature selection method that fits a model and removes the weakest feature (or features) until the specified number of features is reached.

How can feature reduction help in improving the accuracy of decision tree?

Feature Selection

1. Reduces Overfitting: Less redundant data means less opportunity to make decisions based on noise.
2. Improves Accuracy: Less misleading data means modeling accuracy improves.
3. Reduces Training Time: Less data means that algorithms train faster.

How decision tree can be used for 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 does feature extraction work?

Feature Extraction aims to reduce the number of features in a dataset by creating new features from the existing ones (and then discarding the original features). These new reduced set of features should then be able to summarize most of the information contained in the original set of features.