Unstructured Data Classification Question Answer - MCQ Village

Unstructured Data Classification Question Answer

Unstructured Data Classification Question Answer | Unstructured Data Classification TCS Fresco Play Dumps | Unstructured Data Classification Multiple choice question | Unstructured Data Classification Objective type question

1.What kind of classification is our case study ‘Spam Detection’?

Answer : Binary

2.Which pre-processing technique is used to remove the most commonly used words?

Answer : Stopword removal

3.The cross-validation technique is used to evaluate a classifier by dividing the data set into a training set to train the classifier and a testing set to test the same.

Answer : True

4.True Positive is when the predicted instance and the actual instance are not negative.

Answer : True

5.True Negative is when the predicted instance and the actual instance are positive

Answer : False

6.An algorithm that counts how many times a word appears in a document is __

Answer : Bag-of-Words (BOW)

7.Pruning is a technique associated with __

Answer : Decision tree

8.Stemming and lemmatization give the same result.

Answer : False

9.What is the output of the following command: print(sentiment_analysis_data[‘label’].unique())

Answer : [1 0]

10.The most widely used package for machine learning in Python is _

Answer : sklearn

11.In Supervised learning, class labels of the training samples are __

Answer : Known

12.Select the pre-processing technique(s) from the following.

Answer : All the options

13.What command should be given to tokenize a sentence into words?

Answer : from nltk.tokenize import word_tokenize, Word_tokens =word_tokenize

14.The following are performance evaluation measures, except __

Answer : Decision Tree

15.Images and documents are examples of _______

Answer : Unstructured data

16.Choose the correct sequence for classifier building from the following.

Answer : Initialize -> Train -> Predict -> Evaluate

17.Which of the given hyperparameters, when increased, may cause the random forest to overfit the data?

Answer : Depth of Tree

18.The fit (X, y) is used to __

Answer : Train the classifier

19.What does the command sentiment_analysis_data[‘label’].value_counts() return?

Answer : The count of unique values in the ‘label’ column

20.What is the purpose of lemmatization?

Answer : To convert words into a proper base form

21.Clustering is supervised classification.

Answer : False

22.Supervised learning differs from unsupervised learning as supervised learning requires __

Answer : Labeled data

23.To view the first 3 rows of the dataset, which of the following commands is used?

Answer : sentiment_analysis_data.head(3)

24.Inverse Document frequency is used in the term-document matrix.

Answer : True

25.Can we consider sentiment classification as a text classification problem?

Answer : Yes

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