What is ensemble learning

Ensemble learning is the process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem. Ensemble learning is primarily used to improve the (classification, prediction, function approximation, etc.)

why use ensemble learning

We explicitly use ensemble learning to seek better predictive performance, such as lower error on regression or high accuracy for classification. … there is a way to improve model accuracy that is easier and more powerful than judicious algorithm selection: one can gather models into ensembles.

Ensemble-model

Ensemble model using base classifier which uses supervised machine learning algorithm which can accurately predict whether or not the patients in the dataset have diabetes

Ensemble model used

AdaBoost ensemble model

with base estimator as Support Vector Classifier with number of instances of estimator equals to 50.

Random forest ensemble model

with base estimator as Decision tree classifier with 10 Decision tree in the forest and the Entropy used as the parameter for information gain.

Bagging ensemble model

also uses Decision tree classifier as its base estimator with the number of instances as 50.

Ensemble model comparison

Box plot Comparison among three ensemble models