MODULE 2 S1

Question 1

It predicts consecutive numbers (real numbers).

Answer 1

Regression

Question 2

T/F
Simply duplicating the same data points or collecting very similar data will not help.

Answer 2

TRUE

Question 3

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

Answer 3

Overfitting

Question 4

It occurs when you fit a model too closely to the particularities of the training set and obtain a model that works well on the training set but is not able to generalize to new data.

Answer 4

Overfitting

Question 5

This occurs when a model learns the training data too well, including its noise and outliers.

Answer 5

Overfitting

Question 6

If your model is too simple then you might not be able to capture all the aspects of and variability in the data, and your model will do badly even on the training set. Choosing too simple a model is called ____________.

Answer 6

Underfitting

Question 7

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

Answer 7

Predicting

Question 8

Input object : __________
Output value : __________

Answer 8

Feature
Label

Question 9

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

Answer 9

Underfitting

Question 10

It refers to algorithms that address classification problems where the output variable is categorical.

Answer 10

Classification

Question 11

Where labeled training data refers to a dataset that includes both the input data and the corresponding correct output.

Answer 11

Supervised Learning

Question 12

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

Answer 12

Bias

Question 13

Classification : _____________ variable
Regression : ______________ variable

Answer 13

Categorical
Continuous

Question 14

It predicts one of the possible class labels.

Answer 14

Classification

Question 15

Two Types of Classification

Answer 15

Binary Classification
Multiple Classification

Question 16

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

Answer 16

TRUE

Question 17

It refers to the error resulting from sensitivity to the noise / fluctuations in the training data

Answer 17

Variance

Question 18

T/F
The more complex we allow our model to be, the better we will be able to predict on the training data.

Answer 18

TRUE

Question 19

Categories of Supervised Learning

Answer 19

Classification
Regression

Question 20

T/F
The larger variety of data points your data set contains, the more complex a model you can use without overfitting.

Answer 20

TRUE

Question 21

T/F
Model complexity does not depend on the variation of inputs contained in the training dataset.

Answer 21

FALSE
(it is intimately tied)

Question 22

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

Answer 22

Generalization

Question 23

If a model is able to make accurate predictions on unseen data, we say it is able to _____________ from the training set to the test set.

Answer 23

Generalize

Question 24

T/F
The primary objective of the supervised learning technique is to map the input variable with the output variable.

Answer 24

TRUE

Question 25

These are algorithms that handle regression problems where input and output variables have a linear relationship

Answer 25

Regression

Question 26

It refers to when a model is built on the training data and then is able to make accurate predictions on new, unseen data.

Answer 26

Generalization

Question 27

Classification Algorithms

Answer 27

Random Forest Algorithm
Decision Tree Algorithm
Logistic Regression Algorithm
Support Vector Machine Algorithm

Question 28

Regression Algorithms

Answer 28

Simple Linear Regression Algorithm
Multivariate Regression Algorithm
Decision Tree Algorithm
Ridge Algorithm
Lasso Algorithm

Question 29

In supervised learning, market trend is an example of _______________

Answer 29

Regression