Machine Learning Q2:

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Which of the following is a supervised learning algorithm ?

K-means

Support vector machine

DBSCAN

Agglomerative clustering

Clear selection

Which of the following machine learning algorithm is best suited for prediction of house prices ?

K-means

Naive bayes

Linear regression

SVM

Clear selection

Which of the following is/are example of ensemble learning ?

Bagging

Boosting

Both

None of the above

Clear selection

Random forest is an example of ______ ?

Bagging

Boosting

Both

None of the above

Clear selection

Suppose you have 100 decision trees in your random forest model with x1, x2, x3 .... x100 accuracies respectively. What will be the accuracy of the random forest for classification task ?

Geometric mean of x1,x2,x3......x100

Mode of x1,x2,x3......x100

Harmonic mean of x1,x2,x3......x100

Arithmetic mean of x1,x2,x3......x100

Clear selection

Suppose there are 10 relevant movies for a user.If you make a recommendation system which recommends 7 movies to that user in which only 3 are relevant then what is the recall ?

0.5

0.7

0.3

0.4

Clear selection

Suppose there are 10 relevant movies for a user.If you make a recommendation system which recommends 7 movies to that user in which only 3 are relevant then what is the precision ?

0.43

0.7

0.3

0.6

Clear selection

Suppose you have 100 decision trees in your random forest model with x1, x2, x3 .... x100 accuracies respectively. What will be the accuracy of the random forest for regression task ?

Geometric mean of x1,x2,x3......x100

Arithmetic mean of x1,x2,x3......x100

Mode of x1,x2,x3......x100

Harmonic mean of x1,x2,x3......x100

Clear selection

Suppose you are working on a multiple regression problem where you have 10 features.If you want to regularize your linear regression model knowing the fact that all the features are important which of the following regularization would you use ?

L1 regularization

L2 regularization

Anyone

None of the above

Clear selection

In ______, individual trees are independent of each other._______ is the method for improving the performance by aggregating the results of weak learners ??

Boosting

AdaBoost

Bagging

SVM

Clear selection

Which of the following algorithm would you choose for final model building based on the below performance graph ?

Logistic regression

Random forest

Both of the above

None of the above

Clear selection

Which of the following algorithm is based on bootstrap aggregation ?

AdaBoost

Random forest

SVM

Naive bayes

Clear selection

In bagging each individual tree has ___ variance and ____ bias ?

low,high

high,high

low,low

high,low

Clear selection

What if we set C parameter to infinite in SVM ?

Model will gain high bias and low variance

Model will underfit

Model will gain low bias and high variance

None of the above

Clear selection

Which of the following algorithm has smallest training time ?

K-means

KNN

SVM

Linear regression

Clear selection

Which of the following parameter in SVM is responsible for tradeoff between misclassification and simplicity of model ?

gamma

kernel

None of the above

Clear selection

Suppose you have been given the following scenario for training and validation error for Linear Regression.Which of the following scenario would give you the right hyper parameter ?

Clear selection

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