Gradient Descent - Quiz | Analytics

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Gradient Descent - Quiz

How are the parameters updated during Gradient Descent process?

A Sequentially
B Not updated
C One at a time
D Simultaneously

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

A Greedy Problems
B Classification Problems
C Regression Problems
D Real Valued Problems

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

A True
B False

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

A False
B True

____________ measures how far the predictions are from the actual values.

A Bias
B Difference
C Deviation
D Variance

What is the advantage of using an iterative algorithm like gradient descent ? (select the best)

A For Nonlinear regression problems, there is no closed form solution
B Linear regression problems have multiple solutions
C Linear regression problems there is no closed form solution

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

A Regression
B Classification
C Clustering
D None of the options

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

A Classification
B Regression
C Clustering
D None of the options

When an ML Model has a high bias, getting more training data will help in improving the model.

A True
B False

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

A True
B False