DMC DMC Statistical Methods & Data Mining Concepts 2 — Questions and Answers
Question 1: What does 'standard deviation' represent in a dataset?
- The average of all values in the dataset
- The positional middle value of the dataset
- The measure of spread or dispersion around the mean (Correct answer)
- The difference between the maximum and minimum values
Correct answer: The measure of spread or dispersion around the mean
Standard deviation measures how spread out values are around the mean, with a higher value indicating greater variability in the dataset.
Question 2: In data mining, what is 'clustering'?
- Predicting a continuous numerical output value
- Grouping similar data points together without predefined labels (Correct answer)
- Classifying data into known, predefined categories
- Finding co-occurrence rules between items in transactions
Correct answer: Grouping similar data points together without predefined labels
Clustering is an unsupervised learning technique that discovers natural groupings of similar data points without using predefined class labels.
Question 3: A confusion matrix is used to:
- Display relationships between variables visually
- Evaluate the performance of a classification model (Correct answer)
- Visualize correlation between two numerical features
- Summarize descriptive statistics for a dataset
Correct answer: Evaluate the performance of a classification model
A confusion matrix displays true positives, false positives, true negatives, and false negatives to comprehensively evaluate classification model performance.
Question 4: What does 'precision' measure in a classification model?
- The ratio of correctly predicted positives to all actual positives
- The ratio of correctly predicted positives to all predicted positives (Correct answer)
- The overall percentage of correct predictions across all classes
- The recall rate measured against negative predictions
Correct answer: The ratio of correctly predicted positives to all predicted positives
Precision measures the proportion of predicted positive cases that are truly positive, indicating how reliable the model's positive predictions are.
Question 5: Which technique is commonly used to handle imbalanced datasets in classification?
- Normalization
- Discretization
- SMOTE (Synthetic Minority Over-sampling Technique) (Correct answer)
- Principal Component Analysis
Correct answer: SMOTE (Synthetic Minority Over-sampling Technique)
SMOTE generates synthetic examples of the minority class to balance the dataset, improving classifier performance on underrepresented classes.
Question 6: What does 'recall' (sensitivity) measure in a classification model?
- The proportion of predicted positives that are truly positive
- The proportion of actual positives correctly identified by the model (Correct answer)
- The harmonic mean of precision and recall
- The overall accuracy on the test set
Correct answer: The proportion of actual positives correctly identified by the model
Recall measures the proportion of actual positive cases that the model correctly identifies, also known as the true positive rate or sensitivity.
DMC DMC Statistical Methods & Data Mining Concepts 2
What does 'standard deviation' represent in a dataset?