Ensemble Learning – Optimal Machine Learning

Introduction

Machine Learning Model Management: What It Is: Machine Learning (ML)is on the rise. With that, new problems also keep popping up, and ML developers in collaboration with tech companies keep developing new tools to resolve these problems. MLOps is a group of practices for communication and collaboration between operations professionals and data scientists. Application of these practices increases end quality eases the management process, and powers the deployment of ML and deep learning models in large-scale production environments. MLOPs also comprise Model Management. ML models need to be reliable, and meet all business requirements at scale. For this to happen, a logical and easy-to-follow policy for model management is crucial. ML model management is responsible for training, development, deployment, and versioning of ML models.

Ensemble Learning

When we refer to predictive modeling, one algorithmic model may not be sufficient to make the most optimal predictions. One of the most efficient methodologies in Machine Learning (ML) is Ensemble Modeling or Ensembles. Ensemble Modeling is the mixture of various machine learning models that different algorithms or also the same one to make enhanced predictions. It is typically these types of models that win the ML competitions conducted by Kaggle or Netflix.

Categories of Ensemble Learning

Ensemble modeling methods can be divided into various categories. They are:

• Sequential: The model comprises a sequence of steps and with each step, the performance of the model improves by considering the errors of the previous predictions, for example Adaboost.
• Parallel: In contrast to the sequential method, the parallel method trains multiple models on the dataset at the same time. In this method, the models are not dependent on each other. After training on the dataset independently, all the models make forecasts and the final optimal prediction depends on the voting principle for classification problems or averaging for regression problems.

Four Main Sub-Categories of Ensemble Modeling

There are four main sub-categories of ensemble modeling, which are Boosting, Stacking, Bagging, Bucket of Models, and Stacking.

• Boosting: In Boosting, weights are added to the misclassified data from the preceding iteration and thus, enhancement is made in the next iteration. This continues till the model is capable to make good forecasts. This is an example of sequential modeling.
• Stacking: Stacking trains different models on the data and on making new forecasts, it makes use of all these models to make joint predictions.
• Bucket of Models: In this method, different models are trained on the available training dataset and post the hyperparameter tuning, the model that performs best on the test set, is selected for future use.
• Bootstrap Aggregating or Bagging: This is an example of a homogeneous Ensemble. This process selects arbitrary samples of the dataset for n number of times and trains n models of the same type on these n samples (each model trains on a single sample). A voting system then makes predictions from the prediction of all the n models or by taking the average of the predictions of all the models in case of regression.

Conclusion

Thus, it is an undeniable fact that ensemble modeling may grow the accuracy and give us all a better prediction. The combination of these multiple models can compensate and complement the weaknesses of each of the models and thus lead to better predictions.