Ensemble Methods in Machine Learning
Dr. Arundhati Mahesh
Senior Lecturer
Bioinformatics
SRET
SRIHER
, Ensemble Methods: Elegant Techniques to
Produce Improved Machine Learning Results
Ensemble means a group of elements viewed as a whole rather than individually. An Ensemble method creates multiple
models and combines them to solve it in order to produce improved results. Ensemble methods help to improve the
robustness/generalizability of the model. Ensemble methods in machine learning usually produce more accurate
solutions than a single model would.
, Combine Model Predictions Into Ensemble
Predictions
The three most popular methods for combining the predictions from different models are:
● Bagging. Building multiple models (typically of the same type) from different subsamples of the training
dataset.
● Boosting. Building multiple models (typically of the same type) each of which learns to fix the prediction
errors of a prior model in the chain.
● Voting. Building multiple models (typically of differing types) and simple statistics (like calculating the
mean) are used to combine predictions.
Dr. Arundhati Mahesh
Senior Lecturer
Bioinformatics
SRET
SRIHER
, Ensemble Methods: Elegant Techniques to
Produce Improved Machine Learning Results
Ensemble means a group of elements viewed as a whole rather than individually. An Ensemble method creates multiple
models and combines them to solve it in order to produce improved results. Ensemble methods help to improve the
robustness/generalizability of the model. Ensemble methods in machine learning usually produce more accurate
solutions than a single model would.
, Combine Model Predictions Into Ensemble
Predictions
The three most popular methods for combining the predictions from different models are:
● Bagging. Building multiple models (typically of the same type) from different subsamples of the training
dataset.
● Boosting. Building multiple models (typically of the same type) each of which learns to fix the prediction
errors of a prior model in the chain.
● Voting. Building multiple models (typically of differing types) and simple statistics (like calculating the
mean) are used to combine predictions.
