Machine Learning - Exploring the Model | Fresco Play

Question 1: What is the process of dividing each feature by its range called ?

Answer: Feature Scaling

Question 2: Input variables are also known as feature variables.

Answer: True

Question 3: Output variables are known as Feature Variables .

Answer: False

Question 4: ____________ controls the magnitude of a step taken during Gradient Descent .

Answer: Learning Rate

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

Answer: True

Question 6: What is the process of subtracting the mean of each variable from its variable called ?

Answer: Mean Normalization

Question 7: For different parameters of the hypothesis function we get the same hypothesis function.

Answer: False

Question 8: How are the parameters updates during Gradient Descent Process ?

Answer: Simultaneously

Question 9: The result of scaling is a variable in the range of [1 , 10].

Answer: False

Question 10: What is the Learning Technique in which the right answer is given for each example in the data called ?

Answer: Supervised Learning

Question 11: Problems that predict real values outputs are called ?

Answer: Regression Problems

Question 12: Cost function in linear regression is also called squared error function.

Answer: True

Question 13: What is the name of the function that takes the input and maps it to the output variable called ?

Answer: Hypothesis Function

Question 14: For _____________ the error is calculated by finding the sum of squared distance between actual and predicted values

Answer: Regression

Question 15: Lower Decision boundary leads to False Positives during classification

Answer: True

Question 16: Overfiting and Underfitting are applicable only to linear regression problems

Answer: False

Question 17: Reducing the number of features can reduce overfitting

Answer: True

Question 18: ____________ is the line that separates y = 0 and y = 1 in a logistic function.

Answer: Decision Boundary

Question 19: So when a ML Model has high bias, getting more training data will help in improving the model

Answer: True

Question 20: What measure how far the predictions are from the actual values ?

Answer: Variance

Question 21: ____________ function is used as a mapping function for classification problem.

Answer: Sigmoid

Question 22: Problems where discrete valued outputs predicted are called ?

Answer: Real Valued Problems

Question 23: I have a scenario where my hypothesis fits my training set well but fails to generalize for test set. What is this scenario called ?

Answer: Overfitting

Question 24: For ______________ the error is determined by getting the proportion of values miss-classified by the model

Answer: Classification

Question 25: What measures the extent to which the predictions change between various realizations of the model ?

Answer: Variance

Question 26: For an underfit data set the training and the cross validation error will be high.

Answer: False

Question 27: For an overfit data set the cross validation error will be much bigger than the training error.

Answer: True