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Data Mining and Analysis Techniques Flashcards
CompTIA DataX DY0-001 (V1) Flashcards
Study our Data Mining and Analysis Techniques flashcards for the CompTIA DataX DY0-001 (V1) exam with 24+ flashcards. View as flashcards, a searchable table, or as a fun matching game.
| Front | Back |
| Name a popular algorithm for classification in data mining | Decision Tree. |
| Name one major challenge in data mining | Handling missing or incomplete data. |
| What does the term "supervised learning" mean | A type of machine learning where the model is trained on labeled data. |
| What is a confusion matrix in classification | A table used to evaluate the performance of a classification model. |
| What is a decision tree in data mining | A model that makes decisions by splitting data based on feature values. |
| What is a histogram used for in data analysis | To visualize the frequency distribution of a dataset. |
| What is an example of unsupervised learning | Clustering or dimensionality reduction. |
| What is anomaly detection | Identifying data points that deviate from expected behavior or patterns. |
| What is association rule mining | Discovering correlations and relationships between items in transactional datasets. |
| What is clustering in data mining | A technique to group a set of objects based on their similarities. |
| What is data warehousing | The process of collecting and managing data to enable data mining and analysis. |
| What is exploratory data analysis (EDA) | The practice of analyzing datasets visually and statistically to summarize their main characteristics. |
| What is feature selection | The process of reducing the number of input variables when developing a predictive model. |
| What is overfitting in machine learning | A model that performs well on training data but poorly on unseen data. |
| What is PCA (Principal Component Analysis) | A dimensionality reduction technique to emphasize variation in a dataset. |
| What is semantic similarity | A measure of how similar words or phrases are in meaning. |
| What is text mining | Extracting useful information from text data. |
| What is the Apriori algorithm used for | Mining association rules in datasets. |
| What is the definition of data mining | The process of discovering patterns and knowledge from large amounts of data. |
| What is the difference between classification and regression | Classification predicts discrete labels, while regression predicts continuous values. |
| What is the difference between supervised and unsupervised learning | Supervised learning uses labeled data, while unsupervised learning finds hidden patterns in unlabeled data. |
| What is the k-means algorithm used for | Partitioning a dataset into k clusters. |
| What is the purpose of cross-validation | To assess a model’s effectiveness in predicting unseen data. |
| What is the purpose of normalization in data preprocessing | To scale data to fall within a smaller range for consistency and improving accuracy. |
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About the Flashcards
This study set offers a comprehensive review of essential data mining concepts. These flashcards for the CompTIA DataX exam are designed to help you master the key terminology and foundational ideas for discovering patterns and knowledge from large datasets. You'll cover the differences between supervised and unsupervised learning, including core techniques like classification, clustering, and regression. The deck also touches on important data preprocessing steps like normalization and feature selection, along with model evaluation methods such as cross-validation. This is an excellent tool for reinforcing your understanding of the key concepts and algorithms tested on the exam.
Topics covered in this flashcard deck:
- Data Mining Fundamentals
- Supervised & Unsupervised Learning
- Classification & Regression
- Clustering & Association Rules
- Data Preprocessing Methods
- Model Performance Evaluation