Ensemble Learning is taking the predictions of multiple models and assume the output to be having the most votes. When you train multiple Decision Trees each on some random sampling of the dataset and for predictions you take predictions of all the trees, the output class would be the class which gets the most votes. This approach is called Random Forest. Voting classifier is when you train the data on multiple classifier such as Logistic Regression, SVM, RF and other classifiers and the majority vote is the predicted output class ie hard classifier. Voting can also be taken as soft by taking argmax of the outputs.
Missing information is inevitable in real-world data sets. While imputation is well-suited and theoretically sound for statistical inference, its relevance and practical implementation for out-of-sample prediction remains unsettled. We provide a theoretical analysis of widely used data imputation methods and highlight their key deficiencies in making accurate predictions. Alternatively, we propose adaptive linear regression, a new class of models that can be directly trained and evaluated on partially observed data, adapting to the set of available features. In particular, we show that certain adaptive regression models are equivalent to impute-then-regress methods where the imputation and the regression models are learned simultaneously instead of sequentially. We validate our theoretical findings and adaptive regression approach with numerical results with real-world data sets.
Bagging is a simple yet effective design which combines multiple single learners to form an ensemble for prediction. Despite its popular usage in many real-world applications, existing research is mainly concerned with studying unstable learners as the key to ensure the performance gain of a bagging predictor, with many key factors remaining unclear. For example, it is not clear when a bagging predictor can outperform a single learner and what is the expected performance gain when different learning algorithms were used to form a bagging predictor. In this paper, we carry out comprehensive empirical studies to evaluate bagging predictors by using 12 different learning algorithms and 48 benchmark data-sets. Our analysis uses robustness and stability decompositions to characterize different learning algorithms, through which we rank all learning algorithms and comparatively study their bagging predictors to draw conclusions. Our studies assert that both stability and robustness are key requirements to ensure the high performance for building a bagging predictor. In addition, our studies demonstrated that bagging is statistically superior to most single base learners, except for KNN and Naïve Bayes (NB). Multi-layer perception (MLP), Naïve Bayes Trees (NBTree), and PART are the learning algorithms with the best bagging performance.
Boosting is here to help. Boosting is a popular machine learning algorithm that increases accuracy of your model, something like when racers use nitrous boost to increase the speed of their car. Boosting uses a base machine learning algorithm to fit the data. This can be any algorithm, but Decision Tree is most widely used. For an answer to why so, just keep reading.