Computer

Identify the distance matrix used in KNN Algorithm___________________

• All

• KNN 

• The cost of predicting the k nearest neighbours is very high

• K

______ algorithm is widely used for different kinds of learnings because of its uncomplicated and easy to apply nature

• All

• Nearest data points 

• KNN 

• Underfitting

Identify the false statement according to KNN disadavantage_________

• The cost of predicting the k nearest neighbours is very high

• maller k value

• KNN 

• Low accurate predictions 

___________________in KNN is a parameter that refers to the number of nearest neighbours to include in the majority of the voting process.

• All

• K

• KNN 

• Ease to interpret the output

Choosing ______________values for k can be noisy and will have a higher influence on the result.

• Smaller k value

• Low accurate predictions 

• Ease to interpret the output

• Nearest data points 

_________________ is also a lazy learner because it doesn’t learn discriminative function from the training data

• KNN

• Nearest data points 

• Low accurate predictions 

• K

Higher dimension problem in KNN Algorithm _________________________

• Low accurate predictions 

• Nearest data points 

• Ease to interpret the output

• Underfitting

The KNN algorithm can compete with the most accurate models because it makes______________________predictions.

• Low accurate predictions

• The cost of predicting the k nearest neighbours is very high

• Underfitting

• Ease to interpret the output

_____________________________is to evaluate any technique in classification problem.

• Ease to interpret the output

• Low accurate predictions 

• Nearest data points 

• Smaller k value

In KNN, increase in dimension also leads to the problem of __________________________.

• KNN 

• Underfitting

• All

• Smaller k value