# β Quiz M4.01#

Question

What is a linear regression?

a) a model that outputs a continuous prediction as the sum of the values of a

**limited**subset of the input featuresb) a model that outputs a binary prediction based on a linear combination of the values of the input features

c) a model that outputs a continuous prediction as a weighted sum of the input features

*Select a single answer*

Question

Is it possible to get a perfect fit (zero prediction error on the training set)
with a linear classifier **by itself** on a non-linearly separable dataset?

a) yes

b) no

*Select a single answer*

Question

If we fit a linear regression where `X`

is a single column vector, how many
parameters our model will be made of?

a) 1

b) 2

c) 3

*Select a single answer*

Question

If we train a scikit-learn `LinearRegression`

with `X`

being a single column
vector and `y`

a vector, `coef_`

and `intercept_`

will be respectively:

a) an array of shape (1, 1) and a number

b) an array of shape (1,) and an array of shape (1,)

c) an array of shape (1, 1) and an array of shape (1,)

d) an array of shape (1,) and a number

*Select a single answer*

Question

The decision boundaries of a logistic regression model:

a) split classes using only one of the input features

b) split classes using a combination of the input features

c) often have curved shapes

*Select a single answer*

Question

For a binary classification task, what is the shape of the array returned by the
`predict_proba`

method for 10 input samples?

a) (10,)

b) (10, 2)

c) (2, 10)

*Select a single answer*

Question

In logistic regressionβs `predict_proba`

method in scikit-learn, which of the
following statements is true regarding the predicted probabilities?

a) The sum of probabilities across different classes for a given sample is always equal to 1.0.

b) The sum of probabilities across all samples for a given class is always equal to 1.0.

c) The sum of probabilities across all features for a given class is always equal to 1.0.

*Select a single answer*