[Machine Learning] Classification - K-Nearest Neighbours(KNN) | Naive Bayes

[ K-Nearest Neighbours ]

K Nearest Neighbors (KNN) 

=> KNN is a supervised learning classifier, which uses proximity to make classifications or predictions about the grouping of an individual data point.

 

How It Works?

Step 1)

Choose the number K of neighbors

 

Step 2)

Take the K nearst neighbors of the new data point, according to the Euclidean distance

- Euclidean Distance : √((x₂ - x₁)² + (y₂ - y₁)²)  | P1(x₁,y₂) , P2(x₂,y₂) 

 

 

Step 3)

Among these k neighbors count the number of data points in each category

 

Step 4)

Assign the new data point to the category where you counted the most neighbors

 

=> After 4-step, the model is ready! 

 

 

 

 

[ Naive Bayes ]

Naive Bayes : Naive Bayes is naive because it treats all word orders the same. 

- In Naive Bayes, all features(variables) are independent! 

->  Used in Text Classification, Spam Filtering

 

 

 

 

 

Conditional Probability

 

Conditional Probability -  two event A and B are independent 

 

Conditional Probability -  two event A and B are dependent