Contents

  • Introduction
  • How does KNN work?
  • Euclidean Distance
  • Manhattan Distance
  • KNN with the imbalanced dataset.
  • Lazy Learners.
  • Advantages and disadvantages.

How do the KNN algorithm works?



  • Introduction.
  • How does the Random forest algorithm work?
  • Assumption of Random Forest.
  • Advantages and Disadvantages.

Introduction


Overview

Table Of Contents

  • Introduction
  • How does the K-means algorithm work?
  • How to choose the value of K?
  • Elbow Method.
  • Silhouette Method.
  • Advantages of k-means.
  • Disadvantages of k-means.

Introduction


  • AdaBoost is the ensemble learning boosting algorithm that combines several weak classifiers to create a strong classifier.
  • It combines many weak decision trees and it sums the weight of all the weak decision trees to get the final result.
  • The decision tree which we use to consider is called a Stump.

AdaBoost Calculation


  • What is Entropy?
  • Importance of entropy.
  • How to calculate entropy?

What is Entropy?


Linear regression
Linear regression

Example of Linear Regression


Aditya Kumar Pandey

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