Unsupervised learning and model evaluation

Question 1

What is the main goal of unsupervised learning?

a) Predicting future outcomes

b) Grouping or finding patterns in data without labels

c) Testing hypotheses with known outputs

d) Optimizing supervised algorithms

Answer 1

b) Grouping or finding patterns in data without labels

Question 2

What is cluster analysis?

a) A statistical method for finding correlations between variables

b) The process of partitioning data into subsets based on similarity

c) A supervised learning technique for predicting outcomes

d) A method for data cleaning

Answer 2

b) The process of partitioning data into subsets based on similarity

Question 3

In what applications is clustering commonly used?

a) Fraud detection, image recognition, and customer segmentation

b) Regression tasks and time-series analysis

c) Hyperparameter tuning for machine learning models

d) Feature engineering for supervised tasks

Answer 3

a) Fraud detection, image recognition, and customer segmentation

Clustering is an unsupervised learning technique that groups data points into clusters based on their similarity. It is commonly used in tasks where labels are not provided, and the goal is to identify inherent patterns or groupings in the data.

Question 4

What is a partitioning clustering method?

a) A hierarchical decomposition of data into clusters

b) Dividing data into a predefined number of non-overlapping clusters

c) Using density measures to find clusters of arbitrary shapes

d) Grouping based on sequential patterns in time-series data

Answer 4

b) Dividing data into a predefined number of non-overlapping clusters

Question 5

What type of clustering method is k-means?

a) Density-based clustering

b) Grid-based clustering

c) Centroid-based partitioning

d) Hierarchical clustering

Answer 5

c) Centroid-based partitioning

Question 6

What is the first step in the k-means clustering algorithm?

a) Calculate the distances between all data points

b) Assign data points randomly to clusters

c) Select k initial centroids from the dataset

d) Measure the density of each cluster

Answer 6

c) Select k initial centroids from the dataset

Question 7

How does k-means determine cluster membership for a data point?

a) By assigning it to the closest centroid

b) By checking its density within a neighborhood

c) Based on predefined labels

d) Using hierarchical splitting of the dataset

Answer 7

a) By assigning it to the closest centroid

Question 8

What is a major limitation of k-means clustering?

a) It requires labeled data for training

b) It struggles with high-dimensional data and outliers

c) It only works for binary classification problems

d) It is computationally too slow for small datasets

Answer 8

b) It struggles with high-dimensional data and outliers

Question 9

What is frequent pattern mining?

a) Discovering associations and correlations in a dataset

b) A method to predict the next event in a sequence

c) Grouping data points into clusters

d) A supervised learning approach for regression

Answer 9

a) Discovering associations and correlations in a dataset

Question 10

What is an example of a frequent pattern?

a) Clustering customers by demographics

b) Milk and bread frequently bought together in transactions

c) Predicting house prices based on features

d) Finding the best split in a decision tree

Answer 10

b) Milk and bread frequently bought together in transactions

Question 11

What does “support” indicate in market basket analysis?

a) The number of items in a cluster

b) The fraction of transactions containing a specific itemset

c) The probability of an itemset occurring given another itemset

d) The number of times an item appears in the dataset

Answer 11

b) The fraction of transactions containing a specific itemset

Question 12

What does “lift” measure in association rules?

a) The total number of transactions in the dataset

b) The strength of an association relative to its random occurrence

c) The distance between clusters

d) The time complexity of the rule-mining algorithm

Answer 12

b) The strength of an association relative to its random occurrence

Question 13

What is association rule mining?

a) Grouping items in a dataset into clusters

b) Predicting future sales trends

c) Finding relationships between items in transactional data

d) Labeling data points for supervised learning

Answer 13

c) Finding relationships between items in transactional data

Question 14

Which metrics are commonly used to evaluate association rules?

a) Accuracy and precision

b) Support, confidence, and lift

c) Variance and standard deviation

d) Recall and specificity

Answer 14

b) Support, confidence, and lift

Question 15

What is the Apriori algorithm designed for?

a) Clustering high-dimensional datasets

b) Mining frequent itemsets in a dataset

c) Optimizing the parameters of a regression model

d) Predicting sequential patterns in time-series data

Answer 15

b) Mining frequent itemsets in a dataset

Question 16

What is a key concept of the Apriori algorithm?

a) All subsets of a frequent itemset must also be frequent

b) The centroids of clusters are updated iteratively

c) Patterns are evaluated using gradient descent

d) Clusters are assigned based on density

Answer 16

a) All subsets of a frequent itemset must also be frequent

Question 17

In which type of machine learning are labels NOT provided?

a) Supervised learning

b) Unsupervised learning

c) Reinforcement learning

d) None of the above

Answer 17

b) Unsupervised learning

Question 18

What is reinforcement learning?

a) Learning patterns without feedback

b) Learning by interacting with an environment and receiving rewards or penalties

c) Optimizing a decision tree

d) Training neural networks with labeled data

Answer 18

b) Learning by interacting with an environment and receiving rewards or penalties

Question 19

What is overfitting in machine learning?

a) When a model performs well on test data but poorly on training data

b) When a model learns training data too well, including noise, and performs poorly on new data

c) When a model cannot learn patterns from the training data

d) A method to optimize models for higher accuracy

Answer 19

b) When a model learns training data too well, including noise, and performs poorly on new

Question 20

What is underfitting?

a) A model trained too long on training data

b) A model failing to capture patterns in training data, leading to poor predictions

c) A high-variance issue in machine learning models

d) A model performing well on all datasets

Answer 20

b) A model failing to capture patterns in training data, leading to poor predictions

Question 21

What does “goodness of fit” measure?

a) How closely the model matches the ground truth or true values

b) The amount of noise in the data

c) The time taken to train a model

d) The complexity of the model architecture

Answer 21

a) How closely the model matches the ground truth or true values

Question 22

What is training data?

a) Data used to evaluate model performance

b) Data used to build and train a machine learning model

c) A subset of test data

d) Data used only for cross-validation

Answer 22

b) Data used to build and train a machine learning model

Question 23

What is testing data?

a) Data used to fine-tune the hyperparameters

b) A subset of training data

c) Data used to assess a trained model’s performance on unseen data

d) Data used for exploratory data analysis

Answer 23

c) Data used to assess a trained model’s performance on unseen data

Question 24

What is a balanced dataset?

a) A dataset with missing values evenly distributed

b) A dataset where the number of samples in each class is roughly equal

c) A dataset containing only numeric features

d) A dataset with equal amounts of training and testing data

Answer 24

b) A dataset where the number of samples in each class is roughly equal

Question 25

Why is an imbalanced dataset a challenge for machine learning?

a) It reduces the dataset size

b) It may bias the model toward the majority class, reducing predictive performance for the minority class

c) It makes preprocessing impossible

d) It invalidates the evaluation metrics

Answer 25

b) It may bias the model toward the majority class, reducing predictive performance for the minority class

Question 26

What is model validation?

a) A process to assess how well a model performs on unseen data

b) A method to optimize dataset size

c) Training the model with random weights

d) Adjusting hyperparameters to improve accuracy

Answer 26

a) A process to assess how well a model performs on unseen data

Question 27

What is cross-validation?

a) Dividing the data into multiple subsets to train and test the model iteratively

b) A technique to detect overfitting

c) A preprocessing step for balancing data

d) A method to identify noise in datasets

Answer 27

a) Dividing the data into multiple subsets to train and test the model iteratively

Question 28

What is accuracy in machine learning?

a) The fraction of correct predictions out of all predictions made

b) The ability to identify only positive samples

c) The measure of a model’s robustness to noise

d) The trade-off between recall and precision

Answer 28

a) The fraction of correct predictions out of all predictions made

Question 29

What is precision?

a) The fraction of correctly identified positive instances among all predicted positive instances

b) The ability to correctly identify negative instances

c) The fraction of true negatives in the data

d) The ratio of false negatives to true negatives

Answer 29

a) The fraction of correctly identified positive instances among all predicted positive

Question 30

What does the F1 score combine?

a) Accuracy and recall

b) Recall and precision

c) Precision and training error

d) Accuracy and noise reduction

Answer 30

b) Recall and precision

Question 31

What is bias in machine learning?

a) A model’s ability to perform consistently across datasets

b) A systematic error introduced by oversimplified assumptions in a model

c) A variance in data points

d) The effect of noise in data

Answer 31

b) A systematic error introduced by oversimplified assumptions in a model

Question 32

What is concept drift?

a) Changes in the model’s performance over different datasets

b) A shift in the relationship between features and labels over time

c) Variance in data labeling methods

d) Noise reduction during training

Answer 32

b) A shift in the relationship between features and labels over time