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

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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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