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Data Mining and Analysis Techniques Flashcards
CompTIA DataX DY0-001 (V1) Flashcards
| 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. |
This deck focuses on methods and techniques for data mining, analysis, and interpreting datasets to derive meaningful insights.