Stats+Python ISA Test - Quiz 2

Q: Which of the following clustering algorithm is the most sensitive to outliers?

• K-medians clustering algorithm

• K-modes clustering algorithm

• K-means clustering algorithm

• K-medoids clustering algorithm

Q: Consider the image attached and write the code in python needed to create a dataframe that looks as this one?

• DepartmentData=DataFrame({'DOJ': ['12-Mar-2020','2-Feb-2020','15-Jan-2020','23-Apr-2019','14-Sep-2019'],

  'Dep': ['BI','QA','DEV','DEV','QA'],

  'id': [101,102,103,104,105],

  })

• DepartmentData=pd.DataFrame({'DOJ': ['12-Mar-2020','2-Feb-2020','15-Jan-2020','23-Apr-2019','14-Sep-2019'],

  'Dep': ['BI','QA','DEV','DEV','QA'],

  'id': [101,102,103,104,105]

  })

• DepartmentData=pd.DataFrame({DOJ== ['12-Mar-2020','2-Feb-2020','15-Jan-2020','23-Apr-2019','14-Sep-2019'],

  Dep==['BI','QA','DEV','DEV','QA'],

  id== [101,102,103,104,105]

  })

• None of the above

Q: Which of the following should be imported from sklearn to measure the classification or model performance and accuracy?

• metrics

• Pandas

• Numpy

• MASS

Q: Consider the following image depicting association between price and age variable. What would you conclude from the output?

• Positive correlation between variables

• Negative correlation between variables

• Zero correlation between variables

• Cannot say

Q: Which of the following code computes or executes the best fit of the Linear regression model as listed below?

• LREG.predict

• plt.plot.fit

• LREG.fit

• RegModel.fit

Q: Let’s say, a “Linear regression” model perfectly fits the training data (train error is zero). Now, Which of the following statement is true?

• You will always have test error zero

• You can not have test error zero

• Error may be zero, if there is no noise in the test data.

• None of the above

Q: What is bootstrap sampling in statistics and machine learning?

• It is a power option to reboot your final model

• A library used to improve model accuracy in python

• Drawing of sample data repeatedly with replacement, to estimate population parameter

• Drawing of random samples into the population to estimate the best sample

Q: The following visualization shows the fit of three different models (in blue line) on same training data. What can you conclude from these visualizations?

1) The training error in first model is higher when compared to second and third model.

2) The best model for this regression problem is the last (third) model, because it has minimum training error.

3) The second model is more robust than first and third because it will perform better on unseen data.

4) The third model is overfitting data as compared to first and second model.

5) All models will perform same because we have not seen the test data.

• 1 and 3

• 1, 3 and 4

• Only 5

• 1 and 2

Q: Below graphs show two fitted regression lines (A & B) on randomly generated data. Now, I want to find the sum of residuals in both cases A and B. What would it be?

Note:

1) Scale is same in both graphs for both axis.

2) X axis is independent variable and Y-axis is dependent variable.

• A has higher than B

• A has lower than B

• Both have the same

• None of these above

Q: Consider a linear regression problem, using R-squared value to measure goodness-of-fit. Having added another significant variable in the model results in?

• A decrease in the r-squared value

• A decrease in the adjusted-r-squared value

• An increase in both the r-squared values

• A decrease in both r-square and adjusted-r-squared values