What is it? A Random Forest is an ensemble technique which can have capable of performing both regression and classification tasks with the use of multiple decision trees and a technique called Bootstrap and Aggregation, commonly known as bagging. The basic idea behind this is to combine multiple decision trees Read more
How bagging works First of all we will try to understand what Bagging is from the following diagram: Let’s say we have a dataset to train the model, first we need to divide this dataset into number of datasets(atleast more than 2).And then we need to apply `classifier on each Read more
What is Ensemble Learning? We can define Ensemble Learning in this way it uses multiple machine learning models or multiple set of models for the same algorithm which try to make a better prediction. Ensemble Learning model works by training different models on the same dataset and makes prediction iindividually Read more
For detailed theory read An introduction to Statistical Learning: A decision tree is a flowchart-like tree structure where an internal node represents feature, the branch represents a decision rule, and each leaf node represents the outcome. The topmost node in a decision tree is known as the root node. It Read more
What is Support Vector Machines (SVM) We will start our discussion with little introduction about SVM. Support Vector Machine(SVM) is a supervised binary classification algorithm. Given a set of points of two types in N-dimensional place SVM generates a (N−1) dimensional hyperplane to separate those points into two groups. A Read more