**Machine Learning - Exploring the Model Fresco Play MCQs Answers**

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Course Path: Data Science/MACHINE LEARNING METHODS/Machine Learning - Exploring the Model

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**Quiz on Cost Function and Gradient Descent**

1.What is the name of the function that takes the input and maps it to the output variable called?

Map Function

None of the options

Hypothesis Function

Model Function

**Answer: 3)Hypothesis Function**

2.What is the process of dividing each feature by its range called?

Feature Scaling

None of the options

Feature Dividing

Range Dividing

**Answer: 1)Feature Scaling**

3.Problems that predict real values outputs are called __________

Classification Problems

Regression Problems

Real Valued Problems

Greedy Problems

**Answer: 2)Regression Problems**

4.The result of scaling is a variable in the range of [1 , 10].

False

True

**Answer: 1)False**

5.The objective function for linear regression is also known as Cost Function.

False

True

**Answer: 2)True**

6.What is the Learning Technique in which the right answer is given for each example in the data called?

Unsupervised Learning

Supervised Learning

Reinforcement Learning

Right Answer Learning

**Answer: 2)Supervised Learning**

7.Output variables are also known as feature variables.

False

True

**Answer: 1)False**

8.Input variables are also known as feature variables.

False

True

**Answer: 2)True**

9.____________ controls the magnitude of a step taken during Gradient Descent.

Parameter

Step Rate

Momentum

Learning Rate

**Answer: 4)Learning Rate**

10.Cost function in linear regression is also called squared error function.

False

True

**Answer: 2)True**

11.For different parameters of the hypothesis function, we get the same hypothesis function.

False

True

**Answer: 1)False**

12.How are the parameters updated during Gradient Descent process?

Sequentially

Simultaneously

Not updated

One at a time

**Answer: 2)Simultaneously**

**Quiz on Gradient Descent**

1.For ____________, the error is determined by getting the proportion of values misclassified by the model.

Classification

Clustering

None of the options

Regression

**Answer: 1)Classification**

2.High values of threshold are good for the classification problem.

True

False

**Answer: 2)False**

3.Underfit data has a high variance.

True

False

**Answer: 2)False**

4.____________ function is used as a mapping function for classification problems.

Linear

Sigmoid

Convex

Concave

**Answer: 2)Sigmoid**

5.Classification problems with just two classes are called Binary classification problems.

True

False

**Answer: 1)True**

6.Where does the sigmoid function asymptote?

-1 and +1

0 and 1

-inf and +inf

0 and inf

**Answer: 2)0 and 1**

7.Lower Decision boundary leads to False Positives during classification.

False

True

**Answer: 2)True**

8.Linear Regression is an optimal function that can be used for classification problems.

False

True

**Answer: 1)False**

9.For ____________, the error is calculated by finding the sum of squared distance between actual and predicted values.

Regression

None of the options

Classification

Clustering

**Answer: 1)Regression**

10.I have a scenario where my hypothesis fits my training set well but fails to generalize for the test set. What is this scenario called?

Underfitting

Generalization Failure

Overfitting

None of the options

**Answer: 3)Overfitting**

11. What is the range of the output values for a sigmoid function?

[0,.5]

[-inf,+ inf]

[0,1]

[0,inf]

**Answer: 3)[0,1]**

12. ____________ is the line that separates y = 0 and y = 1 in a logistic function.

Divider

None of the options

Separator

Decision Boundary

**Answer: 4)Decision Boundary**

13. Reducing the number of features can reduce overfitting.

False

True

**Answer: 2)True**

14.A suggested approach for evaluating the hypothesis is to split the data into training and test set.

True

False

**Answer: 1)True**

15.Overfitting and Underfitting are applicable only to linear regression problems.

True

False

**Answer: 2)False**

16.Overfit data has high bias.

False

True

**Answer: 1)False**

**ML Exploring the Model - Final Quiz**

1.For an underfit data set, the training and the cross-validation error will be high.

True

False

**Answer: 1)True**

2.For an overfit data set, the cross-validation error will be much bigger than the training error.

True

False

**Answer: 1)True**

3.Problems, where discrete-valued outputs are predicted, are called?

Real Valued Problems

Classification Problems

Greedy Problems

Regression Problems

**Answer: 2)Classification Problems**

4.What measures the extent to which the predictions change between various realizations of the model?

Deviation

Bias

Variance

Difference

**Answer: 3)Variance**

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