Machine Learning

Unstructured Data Classification

⛪ Computer Science and Engineering > Machine Learning

Unstructured Data Classification ➲ Unstructured Data Classification - Quiz

Q 1: Identify the unstructured data from the following.

  • A Excel data
  • B Data from mySQL DB
  • C Image

Q 2: What kind of classification is our case study 'Spam Detection'?

  • A Binary
  • B Multi label
  • C Multi class

Q 3: Which pre-processing technique is used to remove the most commonly used words?

  • A Tokenization
  • B Lemmatization
  • C Stopword removal

Q 4: 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.

  • A True
  • B False

Q 5: True Positive is when the predicted instance and the actual instance are not negative.

  • A True
  • B False

Q 6: True Negative is when the predicted instance and the actual instance are positive.

  • A True
  • B False

Q 7: Can we consider sentiment classification as a text classification problem?

  • A Yes
  • B No

Q 8: 

 a) Download the dataset from https://hrcdn.net/s3_pub/istreet-assets/H4_TQkbOj39HUNoBukluIQ/training.txt  and load it to the variable 'sentiment_analysis_data'.
 b) Give the column names as 'label' and 'message'.
 c) Try out the code snippets and answer the questions.
Is there a class imbalance problem in the given data set?

  • A No
  • B Yes

Q 9: Imagine you have just finished training a decision tree for spam classification, and it is showing abnormal bad performance on both your training and test sets. Assume that your implementation has no bugs. What could be the reason for this problem?

  • A Your decision trees are too shallow
  • B You need to increase the learning rate
  • C You are overfitting
  • D All the options

Q 10: The classification where each data is mapped to more than one class is called ___________

  • A Multi Label Classification
  • B give me correct answer
  • C Multi Class Classification
  • D Binary Classification

Q 11: The following are all classification techniques, except ___________

  • A Random Forest
  • B SGDClassifier
  • C SVM
  • D StratifiedShuffleSplit

Q 12: In a Document Term Matrix (DTM), each row represents ___________

  • A document
  • B TF value
  • C word
  • D TF-IDF value

Q 13: What is the purpose of lemmatization?

  • A To split into sentences
  • B To convert words into a proper base form
  • C To remove redundant words
  • D To convert a sentence into words

Q 14: Choose the correct sequence for classifier building from the following.

  • A Initialize -> Train -> Predict -> Evaluate
  • B Initialize -> Evaluate -> Train -> Predict
  • C Train -> Test -> Initialize -> Predict
  • D None of the options

Q 15: Clustering is supervised classification.

  • A True
  • B False

Q 16: TF-IDF is a feature extraction technique.

  • A True
  • B False

Q 17: 

 a) Download the dataset from https://hrcdn.net/s3_pub/istreet-assets/H4_TQkbOj39HUNoBukluIQ/training.txt  and load it to the variable 'sentiment_analysis_data'.
 b) Give the column names as 'label' and 'message'.
 c) Try out the code snippets and answer the questions.
Which of the following commands is used to view the dataset SIZE, and what is the value returned?

  • A sentiment_analysis_data.size(), (6918, 2)
  • B sentiment_analysis_data.shape, (6918, 3)
  • C sentiment_analysis_data.shape, (6918, 2)
  • D sentiment_analysis_data.size, (6918, 3)

Q 18: Choose the correct sequence from the following.

  • A Data Analysis -> Pre-Processing -> Model Building -> Predict
  • B Pre-Processing -> Model Building -> Predict
  • C Pre-Processing -> Predict -> Train
  • D Data Analysis -> Pre-Processing -> Predict -> Train

Q 19: Email spam data is an example of __________

  • A Unstructured data
  • B Structured data

Q 20: 

 a) Download the dataset from  https://hrcdn.net/s3_pub/istreet-assets/H4_TQkbOj39HUNoBukluIQ/training.txt  and load it to the variable 'sentiment_analysis_data'.
 b) Give the column names as 'label' and 'message'.
 c) Try out the code snippets and answer the questions.
What kind of classification is the given case study (Sentiment Analysis dataset)?

  • A Multi class classification
  • B Binary classification
  • C Multi label classification

Q 21: In a Term Document Matrix (TDM), each row represents ____________

  • A document
  • B TF-IDF value
  • C TF value
  • D word

Q 22: SVM is a _____________

  • A Unsupervised learning algorithm
  • B Weakly supervised learning algorithm
  • C Semi-supervised learning algorithm
  • D Supervised learning algorithm

Q 23: 

 a) Download the dataset from https://hrcdn.net/s3_pub/istreet-assets/H4_TQkbOj39HUNoBukluIQ/training.txt  and load it to the variable 'sentiment_analysis_data'.
 b) Give the column names as 'label' and 'message'.
 c) Try out the code snippets and answer the questions.
What is the output of the following command: print(sentiment_analysis_data['label'].unique())

  • A [yes no]
  • B [true false]
  • C [1 0]
  • D None of the options

Q 24: The most widely used package for machine learning in Python is _________

  • A bottle
  • B jango
  • C sklearn
  • D pillow

Q 25: 

 a) Download the dataset from https://hrcdn.net/s3_pub/istreet-assets/H4_TQkbOj39HUNoBukluIQ/training.txt  and load it to the variable 'sentiment_analysis_data'.
 b) Give the column names as 'label' and 'message'.
 c) Try out the code snippets and answer the questions.
What command should be given to tokenize a sentence into words?

  • A from nltk import sentence_tokenize, Word_tokens =sentence_tokenize(sentence)
  • B from nltk.tokenizer import word_tokenizer, Word_tokens =word_tokenizer(sentence)
  • C from nltk import tokenize_words, Word_tokens =tokenize_words(sentence)
  • D from nltk.tokenize import word_tokenize, Word_tokens =word_tokenize(sentence)

Q 26: The following are performance evaluation measures, except __________

  • A Accuracy Score
  • B Confusion Matrix
  • C Classification Report
  • D Decision Tree