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Top 30+ Machine Learning - Exploring the Model questions and answers

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

  1. Map Function
  2. None of the options
  3. Hypothesis Function
  4. Model Function
  1. Answer: 3)Hypothesis Function

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

  1. Feature Scaling
  2. None of the options
  3. Feature Dividing
  4. Range Dividing

Answer: 1)Feature Scaling

3.Problems that predict real values outputs are called __________

  1. Classification Problems
  2. Regression Problems
  3. Real Valued Problems
  4. Greedy Problems

Answer: 2)Regression Problems

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

  1. False
  2. True

Answer: 1)False

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

  1. False
  2. True

Answer: 2)True

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

  1. Unsupervised Learning
  2. Supervised Learning
  3. Reinforcement Learning
  4. Right Answer Learning

Answer: 2)Supervised Learning

7.Output variables are also known as feature variables.

  1. False
  2. True

Answer: 1)False

8.Input variables are also known as feature variables.

  1. False
  2. True

Answer: 2)True

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

  1. Parameter
  2. Step Rate
  3. Momentum
  4. Learning Rate

Answer: 4)Learning Rate

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

  1. False
  2. True

Answer: 2)True

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

  1. False
  2. True

Answer: 1)False

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

  1. Sequentially
  2. Simultaneously
  3. Not updated
  4. 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.

  1. Classification
  2. Clustering
  3. None of the options
  4. Regression

Answer: 1)Classification

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

  1. True
  2. False

Answer: 2)False

3.Underfit data has a high variance.

  1. True
  2. False

Answer: 2)False

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

  1. Linear
  2. Sigmoid
  3. Convex
  4. Concave

Answer: 2)Sigmoid

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

  1. True
  2. False

Answer: 1)True

6.Where does the sigmoid function asymptote?

  1. -1 and +1
  2. 0 and 1
  3. -inf and +inf
  4. 0 and inf

Answer: 2)0 and 1

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

  1. False
  2. True

Answer: 2)True

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

  1. False
  2. True

Answer: 1)False

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

  1. Regression
  2. None of the options
  3. Classification
  4. 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?

  1. Underfitting
  2. Generalization Failure
  3. Overfitting
  4. None of the options

Answer: 3)Overfitting

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

  1. [0,.5]
  2. [-inf,+ inf]
  3. [0,1]
  4. [0,inf]

Answer: 3)[0,1]

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

  1. Divider
  2. None of the options
  3. Separator
  4. Decision Boundary

Answer: 4)Decision Boundary

13.Reducing the number of features can reduce overfitting.

  1. False
  2. True

Answer: 2)True

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

  1. True
  2. False

Answer: 1)True

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

  1. True
  2. False

Answer: 2)False

16.Overfit data has high bias.

  1. False
  2. 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.

  1. True
  2. False

Answer: 1)True

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

  1. True
  2. False

Answer: 1)True

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

  1. Real Valued Problems
  2. Classification Problems
  3. Greedy Problems
  4. Regression Problems

Answer: 2)Classification Problems

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

  1. Deviation
  2. Bias
  3. Variance
  4. Difference

Answer: 3)Variance

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