Confuse Skull: Structured Data MCQ's

Classification with more than two classes. In multi class classification each sample is assigned to one and only one target label. Eg: An animal can be cat or dog but not both at the same time

Multi label classification:

Classification task where each sample is mapped to a set of target labels (more than one class). Eg: A news article can be about sports, a person, location at the same time.

Supervised classification:

It is a technique where the learning is based on a training set of correctly labeled observations. Eg: Email classification where input data is a set of emails labeled as spam/not spam.

Unsupervised classification:

Grouping the observations into various categories based on some similarity measures. Eg: Grouping of news articles based on the content.

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Structured Data MCQ's

1. Identify the structured data from the following

Data from mySQL DB and Excel

2. What kind of classification is our case study 'Churn Analysis'?

Binary

3. Which command is used to identify the unique values of a column?

unique()

4. Which preprocessing technique is used to make the data gaussian with zero mean and unit variance?

Standardization

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

False

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

True

7. Cross-validation technique will provide accurate results when the training set and the testing set are from two different populations.

True

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

True

9. What kind of classification is the given case study(IRIS dataset)?

Multi class classification

10. Which command is used to select all NUMERIC types in the dataset.

iris_num = iris_data.select_dtypes(include=[numpy.number])

11. How many classes will the following command return(target classes in the dataset) :

classes=list(iris['species'].unique())--3

12. Choose the correct sequence for classifier building from the following:

Initialize -> Train - -> Predict-->Evaluate

13. Identify the command used to view the dataset SIZE and what is the value returned?

iris.shape,(150,6)

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

Yes

15. What does the command iris['species'].value_counts() return?

The total count of elements in iris['species'] column

16. Which of the following is not a technique to process missing values?

One hot encoding

17. Is there a class imbalance problem in the given data set?

Yes

18. Cross-validation causes over-fitting.

False

19. Imputing is a strategy to handle

Missing Values

20. A process used to identify data points that are simply unusual

Anomaly Detection

21. A classifer that can compute using numeric as well as categorical values is

Decision Tree Classifier

22. The commonly used package for machine learning in python is

sklearn

23. What are the advantages of Naive Bayes?

It will converge quicker than discriminative models like logistic regression AND it requires less training data

24. clustering is an example of

unsupervised classification

25. email spam detection is an example of

supervised classification

26. How many new columns does the following command return?

iris_series = pd.get_dummies(iris['Species'])--3

27. Which of the given hyper parameter(s), when increased may cause random forest to over fit the data?

Number of Trees

28. Ordinal variables has clear

logical order

29. High classification accuracy always indicates a good classifier.

True

30. A technique used to depict the performance in a tabular form that has 2 dimensions namely “actual” and “predicted” sets of data.

Confusion Matrix

31. Select pre-processing techniques from the options

All of the option

32. classification where each data is mapped to more than one class is called

Multi Class Classification

33. Which type of cross validation is used for imbalanced dataset?

Stratified Shuffle Split

34. Pruning is a technique associated with

Decision tree

35. The fit(X, y) is used to Train the

Classifier

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