1. Identify the structured data from the following.
View Answer
Data from mySQL DB and Excel
2. What kind of classification is our case study 'Churn Analysis'?
View Answer
Binary
3. Which command is used to identify the unique values of a column?
View Answer
unique()
4. Which preprocessing technique is used to make the data gaussian with zero mean and unit variance?
View Answer
Standardisation
5. 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.
View Answer
True
6. True Negative is when the predicted instance and the actual is positive.
View Answer
False
7. True Positive is when the predicted instance and the actual instance is not negative.
View Answer
True
8. What are the advantages of Naive Bayes?
View Answer
Requires less training data
9. High classification accuracy always indicates a good classifier.
View Answer
True
10. Categorical variables has
View Answer
no logical order
11. Cross-validation technique will provide accurate results when the training set and the testing set are from two different populations.
View Answer
True
12. Choose the correct sequence for classifier building from the following:
View Answer
Initialize -> Train - -> Predict-->Evaluate
13. Which of the given hyper parameter(s), when increased may cause random forest to over fit the data?
View Answer
Depth of Tree
14. To view the first 3 rows of the dataset, which of the following commands are used?Download the dataset from:https://gist.githubusercontent.com/curran/a08a1080b88344b0c8a7/raw/d546eaee765268bf2f487608c537c05e22e4b221/iris.csv to answer the question.
View Answer
iris.head(3)
15. Pruning is a technique associated with
View Answer
Decision tree
16. The commonly used package for machine learning in python is
View Answer
sklearn
17. A classifer that can compute using numeric as well as categorical values is
View Answer
Decision Tree Classifier
18. Can we consider sentiment classification as a text classification problem?
View Answer
yes
19. Let's assume, you are solving a classification problem with highly imbalanced class. The majority class is observed 99% of times in the training data. Which of the following is true when your model has 99% accuracy after taking the predictions on test data. ?
View Answer
For imbalanced class problems, accuracy metric is not a good idea.
20. email spam detection is an example of
View Answer
supervised classification
21. A technique used to depict the performance in a tabular form that has 2 dimensions namely “actual” and “predicted” sets of data.
View Answer
Confusion Matrix
22. What kind of classification is the given case study(IRIS dataset)?Download the dataset from: https://gist.githubusercontent.com/curran/a08a1080b88344b0c8a7/raw/d546eaee765268bf2f487608c537c05e22e4b221/iris.csv to answer the question.
View Answer
Multi class classification
23. Ordinal variables has
View Answer
clear logical order
24. Which command is used to select all NUMERIC types in the dataset.Download the dataset from: https://gist.githubusercontent.com/curran/a08a1080b88344b0c8a7/raw/d546eaee765268bf2f487608c537c05e22e4b221/iris.csv to answer the question.
View Answer
iris_num = iris_data.select_dtypes(include=[numpy.number])
25. The number of categorical attributes in the original dataset.Download the dataset from: https://gist.githubusercontent.com/curran/a08a1080b88344b0c8a7/raw/d546eaee765268bf2f487608c537c05e22e4b221/iris.csv to answer the question.
View Answer
3
26. Which classifier converges easily with less training data?
View Answer
Naive Bayes Classifier
27. Ensemble learning is used when you build component classifiers that are more accurate and independent from each other.
View Answer
true
28. clustering is an example of
View Answer
unsupervised classification
29. Model Tuning helps to increase the accuracy
View Answer
True
30. Imputing is a strategy to handle
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Missing Values
31. classification where each data is mapped to more than one class is called
View Answer
Binary Classification.
32. The fit(X, y) is used to
View Answer
Train the Classifier
33. Supervised learning differs from unsupervised learning as supervised learning requires __________
View Answer
Labeled data
34. Clustering is a supervised classification.
View Answer
False
35. Select the correct option which directly achieve multi-class classification (without support of binary classifiers).
View Answer
K Nearest Neighbor
36. The classification where each data is mapped to more than one class is called ___________
View Answer
Multi Label Classification
37. Email spam data is an example of __________
View Answer
unstructed Data
38. The most widely used package for machine learning in Python is _________
View Answer
sklearn
39. Pruning is a technique associated with __________
View Answer
dt
40. What does the command sentiment_analysis_data['label'].value_counts() return?
View Answer
counts of unique values in the 'label' column
41. Select the pre-processing technique(s) from the following.
View Answer
all
42. Which of the given hyper parameter, when increased, may cause random forest to over fit the data?
View Answer
depth of tree
43. Select the correct statement about Nonlinear classification.
View Answer
Kernel tricks are used by Nonlinear classifiers to achieve maximum-margin hyperplanes.
44. Choose the correct sequence for classifier building from the following.
View Answer
Initialize -> Train - -> Predict-->Evaluate
45. What command should be given to tokenize a sentence into words?
View Answer
from nltk.tokenize import word_tokenize, Word_tokens =word_tokenize(sentence)
46. Choose the correct sequence from the following.
View Answer
Data Analysis -> PreProcessing -> Model Building--> Predict
47. The following are all classification techniques, except ___________
View Answer
StratifiedShuffleSplit
48. The commonly used package for machine learning in python is
View Answer
sklearn
49. How many new columns does the following command return?
View Answer
iris_series = pd.get_dummies(iris['Species'])
50. Download the dataset from: https://gist.githubusercontent.com/curran/a08a1080b88344b0c8a7/raw/d546eaee765268bf2f487608c537c05e22e4b221/iris.csv to answer the question.
View Answer
3
51. Naive Bayes Algorithm is useful for :
View Answer
indepth analysis
52. A process used to identify data points that are simply unusual
View Answer
Anomaly Detection
53. Is there a class imbalance problem in the given data set?
View Answer
no
54. Which of the following is not a technique to process missing values?
View Answer
One hot encoding
55. Images,documents are examples of
View Answer
Unstructured Data
56. email spam detection is an example of
View Answer
The count with unique values in the iris['species'] column
57. Choose the correct sequence for classifier building from the following:
View Answer
Initialize -> Train -> Predict -> Evaluate
58. Identify the command used to view the dataset SIZE and what is the value returned?Download the dataset from: https://gist.githubusercontent.com/curran/a08a1080b88344b0c8a7/raw/d546eaee765268bf2f487608c537c05e22e4b221/iris.csv to answer the question.
View Answer
iris.shape,(150,6)
59. Which type of cross validation is used for imbalanced dataset?
View Answer
K fold
60. To view the first 3 rows of the dataset, which of the following commands are used?Download the dataset from: https://gist.githubusercontent.com/curran/a08a1080b88344b0c8a7/raw/d546eaee765268bf2f487608c537c05e22e4b221/iris.csv to answer the question.
View Answer
iris.head(3)
61. Imagine you have just finished training a decision tree for spam classication and it is showing abnormal bad performance on both your training and test sets. Assume that your implementation has no bugs. What could be reason for this problem.
View Answer
You are overfitting
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