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Lesson 3 Model Training

Total questions: 15

Worksheet time: 8mins

Name
Class
Date
1.

Q1. What is Spearmann Correlation?

a)

Corelating the early man with a spear to model age practices

b)

Strength and direction of the relationship between predicted and actual values

c)

The relation between RMSE and MAE

2.

Q2. What represents How close the regression line is to the true values?

a)

RMSE

b)

MAE

c)

R-Squared

d)

Spearmann Correlation

3.

In which of these segments of the pipeline would you visualize your results?

a)

Score Model

b)

Evaluate Model

4.

Since Automated ML can find the best model, we only need to fine tune its results and looking at other approaches is a waste of time?

a)

True

b)

False

5.

Which of these is the best method for ensemble learning?

a)

Bagging

b)

Boosting

c)

Stacking

d)

Depends on your problem

6.

Identify the false statement

a)

Bagging reduces over fitting

b)

Boosting reduces bias

c)

Stacking increases Accuracy

d)

None of the above

7.

What's the output type in the regression problem?

a)

Numerical or Continuous

b)

Discrete

8.

True negatives are the _____ cases that are _____ predicted as negative by the model?

a)

Negative, Correctly

b)

Positive, Correctly

c)

Negative, Incorrectly

d)

Positive, Incorrectly

9.

Model Evaluation Metrics for Classification?

a)

Accuracy

b)

Precision

c)

Recall

d)

F1 score

10.

ROC full form in the context of Machine learning ROC Curve?

a)

Regressive operating characteristic

b)

Recall operating characteristic

c)

Receiver operating characteristic

d)

Realistic operating characteristic

11.

A _________ is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility?

a)

Graphs

b)

Trees

c)

Decision tree

d)

Neural Networks

12.

The most widely used metrics and tools to assess a classification model are?

a)

Confusion matrix

b)

Cost-sensitive accuracy

c)

Area under the ROC curve

d)

All of the above

13.

How do you handle missing or corrupted data in a dataset?

a)

Drop missing rows or columns

b)

Replace missing values with mean/median/mode

c)

Assign a unique category to missing values

d)

All of the above

14.

 TPTP+FN\frac{TP}{TP+FN}  Whose formula is this?

a)

F1 Score

b)

Accuracy 

c)

Recall

d)

Precision

15.

Proportion of positive cases that are correctly identified by the model?

a)

Accuracy

b)

Recall

c)

F1 score

d)

Precision