Week 3 Quiz 2

Logistic Function gives output in the range

• -1 to 1

• -infinity to 1

• 0 to 1

• 0 to +infinity

Consider the following data X = [ temperature = [12,13.4,15.6,.......,10.5] ], Y = [ Feeling Sick =['Yes', 'No','Yes',........,'No'.].

You need to select a model to predict Y from X. Which is the best Model.

• Linear Regression.

• Support Vector Machine Regression.

• I don't need model to predict I can make a simple rule.

• Logistic Regression.

Gradient Descent for Optimization is used to update the parameters by moving in the direction of

• Increasing Loss.

• It does not moves.

• Gradient Descent is useless.

• Decreasing Loss.

You have a Logistic Regression Model in Python. How can you check if it is a good model or not.

• Observing the Mean Squared Error.

• No need to check the model after fitting.

• Observing Precision, Recall and Accuracy.

• Observing the Code.

If your Learning Rate in Gradient Descent is very large what would happen ?

• It would converge very rapidly.

• It would converge very slowly.

• It would diverge.

• It is the gradient that matters the learning rate is useless.

• a

• b

• None

• a & b

• b

• a

• c

• None

Logistic regression gives output in the range of

• 0-1

• -1 to 1

• -infinity to +infinity

• It gives no output.

• a

• b

• c

• a & b

• b

• a

• c

• None