3.1.4 Ensemble model
Here linearized data which is derived from the WAP tree would be subjected to the training phase based on random Poisson forest. The Random forest model is made up of large set of decision trees and combined them to get an accurate prediction. In decision tree each internal node indicates a test on an attribute. In a decision tree, each branch shows the result of the test. If the node does not have any children then that node is called a leaf node. Every leaf node in the decision tree shows a class label.
The main contibution of this model is that Rather than hunting down the best feature while part a hub, it scans for the best feature among an irregular subset of features. This procedure makes a wide decent variety, which for the most part brings about a superior model.
The random forest algorithm takes less time to train but more time to predict since enormous number of decision trees would cause the model to slow down. In order to speed up the entire process of random forest model, Poisson distribution function is adapted.
Bagging
To reduce its variance
Suppose is a classifier, such as a tree, generating a predicted class label at point x on the basis of our training data S. We take bootstrap samples to bag Cwe bring samples of bootstrap to bag C. Then
Bagging can dramatically decrease the variance in volatile (like trees) processes, leading to better forecast. In C (e.g. a tree), however, any easy structure is lost.
Boosting
Average many trees, each grown to re-weighted versions of the training data.
Final Classifier is weighted by calculating average of classifiers: