Skip to content

Heart Attack Prediction by implementing Feature Selection such as SelectKBest & Recursive Feature Elimination

Notifications You must be signed in to change notification settings

SrinathMurali96/Heart_Attack_Prediction-Using_Feature-Selection_Algorithm

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

7 Commits
 
 
 
 
 
 

Repository files navigation

Heart_Attack_Prediction-Using_Feature-Selection_Algorithm

Feature selection is the process of reducing the number of input variables when developing a predictive model.

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. It reduces overfitting.

The two feature selection methods used are: 1. Recursive Feature Elimination 2. SelectKBest

Recursive Feature Elimination :

The goal of recursive feature elimination (RFE) is to select features by recursively considering smaller and smaller sets of features. First, the estimator is trained on the initial set of features and the importance of each feature is obtained either through a coef_ attribute or through a feature_importances_ attribute.

Then, the least important features are pruned from the current set of features. That procedure is recursively repeated on the pruned set until the desired number of features to select is eventually reached.

For refernce : http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html

SelectKBest:

Select features according to the k highest scores.

f_classif: ANOVA F-value between label/feature for classification tasks.

f_regression: F-value between label/feature for regression tasks.

For refernce : http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectKBest.html

About

Heart Attack Prediction by implementing Feature Selection such as SelectKBest & Recursive Feature Elimination

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages