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Structured Data Classification Fresco Play MCQs Answers

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Course Path: Data Science/MACHINE LEARNING METHODS/Structured Data Classification

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Classification quiz

1.Identify the structured data from the following.

1. Data from mySQL DB and Excel
2. Data from mySQL DB
3. Video clip
4. Excel data
5. Image

Answer: 1)Data from mySQL DB and Excel

Quiz : Classification Type

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

1. Binary
2. Multi label
3. Multi class

Quiz : Unique Command

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

1. unique()
2. distinct()
3. value_counts()
4. shape

Quiz: Preprocessing

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

1. Normalization
2. Standardization
3. Binarization

Quiz on Performance Evaluation Measures

1.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 classifier model.

1. False
2. True

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

1. False
2. True

3.True Positive is when the predicted instance and the actual instance are positive.

1. False
2. True

Quiz-Final Assessment

1.What are the advantages of Naive Bayes?

1. It will converge quicker than the discriminative models like logistic regression
2. It requires less training data
3. Both the options
4. None of the options

2.The commonly used package for machine learning in Python is _________

1. sklearn
2. pillow
3. jango
4. bottle

3.The following are techniques to process missing values, except _______

1. One hot encoding
2. None of the options
3. Dropping missing variables
4. Imputing

4.Choose the correct sequence from the following.

1. PreProcessing -> Model Building -> Predict
2. Preprocessing -> Predict -> Train
3. Data Analysis -> Preprocessing -> Model Building -> Predict
4. Data Analysis -> Preprocessing -> Predict -> Train

Answer: 3)Data Analysis -> Preprocessing -> Model Building -> Predict

5.High classification accuracy always indicates a good classifier.

1. False
2. True

6.Ensemble learning is used when you build component classifiers that are more accurate and independent of each other.

1. False
2. True

7.A technique used to depict the performance in a tabular form that has 2 dimensions namely actual and predicted sets of data is ________

1. Classification Accuracy
2. Cross Validation
3. Classification Report
4. Confusion Matrix

8.Email spam detection is an example of ________

1. Supervised classification
2. Unsupervised classification

9.Ordinal variables have __________

1. A clear logical order
2. No logical order

10.Naive Bayes Algorithm is useful for ____________

1. None of the options
2. Slow scanning
3. In-depth analysis
4. Fast to classify

11.Imputing is a strategy to handle ____________

1. Standardization
2. Class Imbalance
3. Missing Values

12.A process used to identify unusual data points is _________

1. Over Fitting
2. Anomaly Detection
3. Under fitting

13.Pruning is a technique associated with _________

1. SVM
2. Logistic regression
3. Decision tree
4. Linear regression

14.What is the number of categorical attributes in the original dataset?

1. 2
2. 0
3. 1
4. 3

15.How many classes will the following command return, while printing the length of classes?

(target classes in the dataset) : classes=list(iris['species'].unique())

1. 3
2. 4
3. 1
4. 2

16.The fit(X, y) is used to _________

1. Initialize the classifier
2. Train the classifier
3. Evaluate the classifier
4. Test the classifier

17.How many new columns does the following command will return while printing the shape of the below data frame?

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

1. 3
2. 2
3. 4
4. 1

18.Which classifier converges easily with less training data?

1. Random Forest Classifier
2. SVM Classifier
3. Decision Tree Classifier
4. Naive Bayes Classifier

19.Let's assume you are solving a classification problem with a highly imbalanced class. The majority class is observed 99% of the time in the training data.

Which of the following is true when your model has 99% accuracy after taking the predictions on test data?

1. For imbalanced class problems, the accuracy metric is a good idea.
2. For imbalanced class problems, the accuracy metric is not a good idea.
3. For imbalanced class problems, precision and recall metrics are not good.

Answer: 2)For imbalanced class problems, the accuracy metric is not a good idea.

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

1. iris.get(3)
2. iris.top(3)
4. iris.select(3)

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