✅ 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 features
b) 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
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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
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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
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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
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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
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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)
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Question
In logistic regression’s 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.
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