What is K-Means Clustering? K-means clustering is an unsupervised learning algorithm that can be used for solving clustering problems in machine learning. K-means clustering takes a bunch of unlabeled data and groups them into k clusters. The clustering is done in...
What is AdaBoost, and how does it work? AdaBoost or Adaptive Boosting is an ensemble technique using which multiple decision trees can be ensembled to solve a regression or classification problem. In our previous video, we discussed bagged decision trees. In this...
What is bagging in machine learning? Let’s say we want to solve a classification problem using machine learning. For example, let’s say we are reading the Pima Indians Diabetes dataset. Now, the dataset contains various predictor variables such as the number of...
What are Extra Trees? The Extra Trees algorithm creates a large number of unpruned decision trees and makes predictions based on that. For a regression problem, the Extra Trees algorithm takes the average of all the predictions made by the decision trees. And for a...
A Support Vector Machine (SVM) uses a supervised learning method to solve regression or classification problems. Let’s say a dataset has n features. So, we can think of an n-dimensional space formed by the features. And a hyperplane is an (n-1) dimensional subspace...
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