Data Mining 1

Question 1

Series of tasks, activities, or operations to achieve a goal or an outcome

Answer 1

Process

Question 2

Combination of hardware and software to facilitate or automate processes

Answer 2

Technology

Question 3

Discrete measurement, fact, or observation representing a real-world process

Answer 3

Data

Question 4

the mathematical discipline that studies the methods of collecting, analyzing, and interpreting data.

Answer 4

Statistics

Question 5

specific collection of items of interest

Answer 5

Population

Question 6

subset or subcollection of the population

Answer 6

Sample

Question 7

two scopes of data

Answer 7

Sample & Population

Question 8

Logic is built based on business rules

Answer 8

Traditional Rule-Based AI

Question 9

Logic is built by modelling and training data

Answer 9

Machine Learning

Question 10

Input and sometimes output data are provided to a machine which will build a logic based on mathematical rules

Answer 10

Machine Learning

Question 11

Machine learning algorithms in which the training data includes both input and output

Answer 11

Supervised Machine Learning

Question 12

Inputs are called

Answer 12

feature values

Question 13

outputs are called

Answer 13

label values

Question 14

the label predicted by the model is a numeric value

Answer 14

Regression

Question 15

the model predicts whether a record is an instance of a specific class or category

Answer 15

Binary Classification

Question 16

the model predicts whether a record is an instance of one of multiple classes or categories

Answer 16

Multiclass Classification

Question 17

Training data consists only of input without any known output

Answer 17

Unsupervised Machine Learning

Question 18

the model identifies similarities between observations based on their features and groups them into discrete clusters

Answer 18

Clustering

Question 19

A model that groups existing customers into clusters based on age, location, gender, social media usage, and purchasing behavior.

Answer 19

Clustering

Question 20

A model that classifies whether a social media post is positive, negative, or neutral.

Answer 20

Multiclass Classification

Question 21

A model that predicts whether a customer will cancel their subscription.

Answer 21

Binary Classification

Question 22

A model that predicts the price of an apartment based on the size, number of rooms, barangay, and date of building.

Answer 22

Regression

Question 23

Used to train the model, data where the algorithm learns patterns from

Answer 23

Training Data

Question 24

Used to evaluate the model

Answer 24

Test Data

Question 25

Proportion of predictions that the model got right

Answer 25

Accuracy

Question 26

Proportion of predicted positive cases where the true label is actually positive

Answer 26

Precision

Question 27

Proportion of positive cases that the model identified correctly

Answer 27

Recall

Question 28

Overall metric combining Recall and Precision

Answer 28

F1 Score

Question 29

a lazy learning algorithm, predicts the class of a data point based on the majority class of its k nearest neighbors

Answer 29

k-NN classifier

Question 30

predicts the probability that a given data point belongs to a particular class, uses the logistic function

Answer 30

Logistic Regression

Question 31

an S-shaped curve, used to represent logistical regression

Answer 31

logistic function

Question 32

occurs when one class is significantly more frequent than the other

Answer 32

Class Imbalance

Question 33

reducing the number of instances in the majority class by removing samples until the classes are balanced.

Answer 33

Undersampling

Question 34

increasing the number of instances in the minority class by duplicating samples or generating new synthetic examples.

Answer 34

Oversampling

Question 35

Generates synthetic samples for the minority class by interpolating between existing samples

Answer 35

SMOTE (Synthetic Minority Oversampling Technique)

Question 36

Cons of Oversampling

Answer 36

Oversampling can cause overfitting, especially with random oversampling.

Question 37

Cons of Undersampling

Answer 37

Important information from the majority class may be lost, potentially underfitting the model.