MLA FA

Question 1

The two phases of supervised ML process: Training, ________.

Answer 1

PREDICTING

Question 2

Logistic Regression is an example of a regression algorithm.

Answer 2

FALSE

Question 3

The _____ refers to the error from having wrong / too simple assumptions in the learning algorithm.

Answer 3

BIAS

Question 4

Its primary objective is to map the input variable with the output variable.

Answer 4

Supervised Learning

Question 5

These concepts helps to understand how well a model performs: Overfitting, Underfitting, _________.

Answer 5

GENERALIZATION

Question 6

If your model performs well on the training set but poorly on the validation set.

Answer 6

Overfitting

Question 7

When the model fits too closely to the training dataset.

Answer 7

Generalization

Question 8

In k-NN, High Model Complexity is underfitting.

Answer 8

FALSE

Question 9

K-nearest neighbors make a prediction for a new data point by finding the data that match from the training dataset.

Answer 9

FALSE

Question 10

In k-NN, Low Model Complexity is:

Answer 10

Underfitting

Question 11

In k-NN, when you choose a small value of k (e.g., k=1), the model becomes more complex.

Answer 11

TRUE

Question 12

There is a regression variant of the k-nearest neighbors algorithm.

Answer 12

TRUE

Question 13

In k-NN, High Model Complexity is:

Answer 13

Overfitting

Question 14

There is a regression variant of the k-nearest neighbors algorithm.

Answer 14

FALSE

Question 15

When comparing training set and test set scores, we find that we predict very accurately on the training set, but the R2 on the test set is much worse. This is a sign of underfitting.

Answer 15

FALSE

Question 16

When comparing training set and test set scores, we find that we predict very accurately on the training set, but the R2 on the test set is much worse. This is a sign of:

Answer 16

Overfitting

Question 17

Ridge regression is a linear regression model that controls complexity to avoid overfitting.

Answer 17

TRUE

Question 18

Lasso uses L1 Regularization.

Answer 18

TRUE

Question 19

The ‘slope’ parameter is also called _______ or coefficients.

Answer 19

Weight

Question 20

Linear Regression is also known as Ordinal Least Squares.

Answer 20

FALSE

Question 21

In supervised learning, market trend analysis is an example of:

Answer 21

REGRESSION

Question 22

A model that performs poorly on both training and new data because it hasn’t learned enough from the training data.

Answer 22

Underfitting

Question 23

Classification algorithms address classification problems where the output variable is categorical.

Answer 23

TRUE

Question 24

This refers to the error resulting from sensitivity to the noise in the training data.

Answer 24

Not in the options

Question 25

The ‘k’ in k-Nearest neighbors refers to the new closest data point

Answer 25

FALSE

Question 26

In k-NN, Euclidean distance (by default) is used to choose the right distance measure.

Answer 26

TRUE

Question 27

The ________ is the sum of the squared differences between the predictions and the true values.

Answer 27

Mean Squared Error

Question 28

In Ridge regression is α (alpha) is lesser, the penalty becomes larger.

Answer 28

FALSE

Question 29

Ridge is generally preferred over Lasso, but if you want a model that is easy to analyze and understand then use Lasso.

Answer 29

TRUE

Question 30

Dichotomous classes means Yes or No.

Answer 30

TRUE

Question 31

Linear models make a prediction using a linear function of the input features.

Answer 31

TRUE

Question 32

The ‘offset’ parameter is also called slope.

Answer 32

FALSE