mla

Question 1

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

Answer 1

Prediction

Question 2

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

Answer 2

generalization

Question 3

When the model fits too closely to the training dataset.
Group of answer choices

Overfitting

Generalization

Underfitting

Answer 3

Overfitting

Question 4

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

Regression

Correlation

Prediction

Classification

Answer 4

Regression

Question 5

Logistic Regression is an example of a regression algorithm.
Group of answer choices

True

False

Answer 5

FALSE

Question 6

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

Answer 6

bias

Question 7

If your model performs well on the training set but poorly on the validation set.
Group of answer choices

Underfitting

Generalization

Overfitting

Answer 7

Overfitting

Question 8

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

True

False

Answer 8

TRUE

Question 9

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

True

False

Answer 9

TRUE

Question 10

In k-NN, High Model Complexity is underfitting.
Group of answer choices

True

False

Answer 10

FALSE

Question 11

The ‘k’ in k-Nearest neighbors refers to the new closest data point.
Group of answer choices

True

False

Answer 11

FALSE

Question 12

In k-NN, High Model Complexity is:
Group of answer choices

Overfitting

Underfitting

Answer 12

Overfitting

Question 13

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

True

False

Answer 13

FALSE

Question 14

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

True

False

Answer 14

TRUE

Question 15

In Ridge regression is α (alpha) is lesser, the penalty becomes larger.
Group of answer choices

True

False

Answer 15

FALSE

Question 16

Linear Regression is also known as Ordinal Least Squares.
Group of answer choices

True

False

Answer 16

FALSE

Question 17

The ‘slope’ parameter is also called _______ or coefficients.
Group of answer choices

Weight

Length

Mean

Median

Answer 17

Weight

Question 18

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

True

False

Answer 18

TRUE

Question 19

Lasso uses L1 Regularization.
Group of answer choices

True

False

Answer 19

TRUE

Question 20

Ridge regression is a linear regression model that controls complexity to avoid overfitting.
Group of answer choices

True

False

Answer 20

True

Question 21

Its primary objective is to map the input variable with the output variable.
Group of answer choices

Correlation

Classification

Unsupervised Learning

Supervised Learning

Answer 21

Supervised Learning

Question 22

Dichotomous classes means Yes or No.
Group of answer choices

True

False

Answer 22

TRUE

Question 23

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

Underfitting

Generalization

Overfitting

Answer 23

Underfitting

Question 24

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

Answer 24

TRUE

Question 25

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

True

False

Answer 25

TRUE

Question 26

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

True

False

Answer 26

TRUE

Question 27

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.
Group of answer choices

True

False

Answer 27

FALSE

Question 28

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

Median error

Mean error

Total R

Mean Squared Error

Not in the options

Answer 28

Mean Squared Error

Question 29

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

True

False

Answer 29

TRUE