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: