Machine Learning Internship test:
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Which of the following is a supervised learning algorithm ?
Kmeans
Support vector machine
DBSCAN
Agglomerative clustering
Clear selection
Which of the following machine learning algorithm is best suited for prediction of house prices ?
Kmeans
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 ?
Kmeans
KNN
SVM
Linear regression
Clear selection
Which of the following parameter in SVM is responsible for tradeoff between misclassification and simplicity of model ?
gamma
C
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 ?
1
2
3
4
5
Clear selection
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