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

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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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