Machine Learning - Practice Test

[#141] While using . . . . . . . . all labels are turned into sequential numbers.

(A) LabelEncoder class

(B) LabelBinarizer class

(C) DictVectorizer

(D) FeatureHasher

(E) LabelEncoder class

(F) LabelBinarizer class

(G) DictVectorizer

(H) FeatureHasher

Correct Answer

(A) LabelEncoder class
(E) LabelEncoder class

[#142] . . . . . . . . is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.

(A) Removing the whole line

(B) Creating sub-model to predict those features

(C) Using an automatic strategy to input them according to the other known values

(D) All above

(E) Removing the whole line

(F) Creating sub-model to predict those features

(G) Using an automatic strategy to input them according to the other known values

(H) All above

Correct Answer

(A) Removing the whole line
(E) Removing the whole line

[#143] The network that involves backward links from output to the input and hidden layers is called

(A) self organizing maps

(B) perceptrons

(C) recurrent neural network

(D) multi layered perceptron

(E) self organizing maps

(F) perceptrons

(G) recurrent neural network

(H) multi layered perceptron

Correct Answer

(C) recurrent neural network
(G) recurrent neural network

[#144] Which of the following is true about averaging ensemble?

(A) it can only be used in classification problem

(B) it can only be used in regression problem

(C) it can be used in both classification as well as regression

(D) none of these

(E) it can only be used in classification problem

(F) it can only be used in regression problem

(G) it can be used in both classification as well as regression

(H) none of these

Correct Answer

(C) it can be used in both classification as well as regression
(G) it can be used in both classification as well as regression

[#145] Which of the following parameters can be tuned for finding good ensemble model in bagging based algorithms? 1. Max number of samples 2. Max features 3. Bootstrapping of samples 4. Bootstrapping of features

(A) 1 and 3

(B) 2 and 3

(C) 1 and 2

(D) all of above

(E) 1 and 3

(F) 2 and 3

(G) 1 and 2

(H) all of above

Correct Answer

(D) all of above
(H) all of above

Machine Learning - Study Mode

[#141] While using . . . . . . . . all labels are turned into sequential numbers.

Correct Answer

[#142] . . . . . . . . is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.

Correct Answer

[#143] The network that involves backward links from output to the input and hidden layers is called

Correct Answer

[#144] Which of the following is true about averaging ensemble?

Correct Answer

[#145] Which of the following parameters can be tuned for finding good ensemble model in bagging based algorithms? 1. Max number of samples 2. Max features 3. Bootstrapping of samples 4. Bootstrapping of features

Correct Answer