Machine Learning - Exploring the Model MCQs Solutions | TCS Fresco Play

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

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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

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

Feature Scaling

None of the options

Feature Dividing

Range Dividing

3.Problems that predict real values outputs are called __________

Classification Problems

Regression Problems

Real Valued Problems

Greedy Problems

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

False

True

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

False

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

7.Output variables are also known as feature variables.

False

True

8.Input variables are also known as feature variables.

False

True

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

Parameter

Step Rate

Momentum

Learning Rate

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

False

True

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

False

True

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

Sequentially

Simultaneously

Not updated

One at a time

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

Classification

Clustering

None of the options

Regression

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

True

False

3.Underfit data has a high variance.

True

False

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

Linear

Sigmoid

Convex

Concave

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

True

False

6.Where does the sigmoid function asymptote?

-1 and +1

0 and 1

-inf and +inf

0 and inf

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

False

True

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

False

True

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

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

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

[0,.5]

[-inf,+ inf]

[0,1]

[0,inf]

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

Divider

None of the options

Separator

Decision Boundary

13. Reducing the number of features can reduce overfitting.

False

True

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

True

False

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

True

False

16.Overfit data has high bias.

False

True

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

True

False

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

True

False

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

Real Valued Problems

Classification Problems

Greedy Problems

Regression Problems

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

Deviation

Bias

Variance

Difference