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Data Mining Techniques Flashcards
CompTIA Data+ DA0-002 (V2) Flashcards
Study our Data Mining Techniques flashcards for the CompTIA Data+ DA0-002 (V2) exam with 10+ flashcards. View as flashcards, a searchable table, or as a fun matching game.
| Front | Back |
| What is association rule mining? | A technique to uncover interesting relationships and frequent itemsets among variables in a dataset |
| What is classification in data mining? | A technique that assigns data points to predefined categories based on extracted features |
| What is clustering in data mining? | A method that groups similar data points together without using predefined labels |
| What is cross-validation in data mining? | A technique that splits data into subsets to test model performance and ensure it generalizes well to unseen data |
| What is data mining? | The process of discovering patterns, correlations and useful insights in large datasets using various algorithms |
| What is dimensionality reduction? | A process that reduces the number of input variables to simplify models and counter the curse of dimensionality |
| What is feature selection? | The process of identifying and choosing the most relevant variables for building an effective predictive model |
| What is overfitting in data mining? | A scenario where a model captures noise along with the underlying pattern, resulting in poor performance on new data |
| What is regression analysis in data mining? | A method used to predict continuous outcomes by modeling relationships between dependent and independent variables |
| What is the difference between supervised and unsupervised learning? | Supervised learning uses labeled data to train models while unsupervised learning identifies patterns in unlabeled data |
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About the Flashcards
Flashcards for the CompTIA Data+ exam guide you through the essential language and analytical techniques of modern data mining. Each card distills concepts such as classification models, clustering algorithms, association rule discovery, and regression analysis into clear questions and answers, allowing quick recall during study sessions.
Use the deck to reinforce understanding of how supervised and unsupervised learning differ, why overfitting harms model accuracy, and how dimensionality reduction, feature selection, and cross-validation improve generalization. The concise format helps you memorize definitions, compare methods, and link theory to practical critical exam scenarios.
Topics covered in this flashcard deck:
- Data mining fundamentals
- Supervised vs. unsupervised learning
- Classification & regression
- Clustering & association rules
- Model evaluation techniques
- Dimensionality reduction