arbaazt21 / KNN

KNN (K-Nearest Neighbor)

• KNN is a Supervised Machine Learning Algorithm.
• It is a Simplest algorithm.
• It is Distance-based algorithm.
• KNN is used for both Classification and Regression.
• But it is widely used for classification problems.
• It is also called as Voting-Classifier.
• It is NON-PARAMETRIC.
• KNN is also called as LAZY-LEARNER Algortithm, bcoz it doesn't learn from the Training Dataset immediately, instead it stores the dataset & at the time of classification it performs an action on the dataset.

Working of KNN

• Step-1: Select the number K of the neighbors
• Step-2: Calculate the Euclidean distance of K number of neighbors
• Step-3: Take the K nearest neighbors as per the calculated Euclidean distance.
• Step-4: Among these k-neighbors, count the number of the data points in each category.
• Step-5: Assign the new data points to that category for which the no. of the neighbors is maximum.
• Step-6: Our model is ready

KNN is a Distance-based Algorithm,

So for calculating Distance, there are two methods:-

1) Eulidean Distance (used by default):-
    it calculates the distance in straigth-line (linear-line)
    Faster
    Accuracy is GOOD.
    
2) Manhattan Distance (Taxicab) or (City-block):- 
    it calculates the distance in travelling to other points also.
    Time Consuming.
    Accuracy is BEST.

Important in KNN

• Normalization or Standardization
• Hyperparameter-Tunning

Advantages of KNN Algorithm:

• It is simple to implement.
• It is robust to the noisy training data
• It can be more effective if the training data is large.