Skip to main content

KNN Interview Questions

                     KNN interview questions

1)Which of the following distance metric can not be used in k-NN?

A) Euclidean Distance

B) Manhatten Distance

c) Hamming Distance

E) Minkowski Distance

F) Jaccard Distance

G) All the above

Answer:- G

All of these distance metric can be used as a distance metric for KNN

2)Knn is for regression or classification?

Answer:- Knn is used for both classification and regression problems.

3) When we use Manhatten Distance?

Answer:-Manhatten distance is used for continuous variables.


4)You have given the following 2 statements, find which of these options is/are true in case of k-NN?

  1. In the case of very large value of k, we may include points from other classes into the neighborhood, so it leads to overfitting.
  2. In case of too small value of k the algorithm is very sensitive to noise.(it will affect our model performance).
Answer:-The above two points are answers.

5) which algorithm do you prefer to deal with missing values?
     a) Linear Regression
     b) Logistic Regression
     c) KNN
Answer:-KNN ( while treating missing values knn gives nearest neighbors value to fill the missing term).

6) overfitting in k-nn?
Answer:-In an overfitted module, it seems to be performing well on training data, but it is not generalized enough to give the same results on new data.

 

 

Comments

Post a Comment

Popular posts from this blog

SUPPORT VECTOR MACHINE

                 SUPPORT VECTOR MACHINE:- Support vector machine:-it is a type of supervised learning algorithm it is used to solve both classification and regression problem. Note :- It is mostly used for classification problems. what we are going to learn in SVM: a) Support vectors b) Hyperplane c) Marginal Distance d) Linear Separable e) Non-linear separable f) support kernels NOw we will discuss everything in detail. Hyper plane:- in the above diagram, we have drawn three lines(A, B, C) separating two data points (stars and reds) The lines (A, B, C) are called Hyperplanes. Note:- “Select the hyper-plane which segregates the two classes better” i.e  above there are three hyperplanes how to select the best hyperplane? b)Marginal Distance:- When we draw a hyperplane the plane creates two new(------) dotted lines one line above the hyperplane and one line below the hyperplane line. see the below image you will get an ...
Post a Comment
Read more

K-NN

                           K-Nearest Neighbour The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems. KNN means in short Similar things near to each other. The KNN algorithm uses ‘ feature similarity ’ to predict the values of any new data points. I am going to explain this knn with a simple example:- In the above table, we have S.No, Height, Weight & Age in our table for S.No.5 the weight is missing, So now we need to predict the weight of the person based on his Height and Age. graph example in the above graph, X_axis represents the age and the y_axis represents the Height of a person. in the above graph, I write 5 numbers, in that  4 values have output and one id not having output now see How KNN help us. the 5th number I want to predict which is circled. hint:-By seeing th...
Post a Comment
Read more