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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. |
Front
What is semantic similarity
Click the card to flip
Back
A measure of how similar words or phrases are in meaning.
Front
What is feature selection
Back
The process of reducing the number of input variables when developing a predictive model.
Front
What is the difference between classification and regression
Back
Classification predicts discrete labels, while regression predicts continuous values.
Front
Name a popular algorithm for classification in data mining
Back
Decision Tree.
Front
What is the k-means algorithm used for
Back
Partitioning a dataset into k clusters.
Front
What is data warehousing
Back
The process of collecting and managing data to enable data mining and analysis.
Front
What is anomaly detection
Back
Identifying data points that deviate from expected behavior or patterns.
Front
What is a confusion matrix in classification
Back
A table used to evaluate the performance of a classification model.
Front
What is the purpose of normalization in data preprocessing
Back
To scale data to fall within a smaller range for consistency and improving accuracy.
Front
What does the term "supervised learning" mean
Back
A type of machine learning where the model is trained on labeled data.
Front
What is a decision tree in data mining
Back
A model that makes decisions by splitting data based on feature values.
Front
What is the purpose of cross-validation
Back
To assess a model’s effectiveness in predicting unseen data.
Front
What is an example of unsupervised learning
Back
Clustering or dimensionality reduction.
Front
What is a histogram used for in data analysis
Back
To visualize the frequency distribution of a dataset.
Front
What is association rule mining
Back
Discovering correlations and relationships between items in transactional datasets.
Front
What is the definition of data mining
Back
The process of discovering patterns and knowledge from large amounts of data.
Front
Name one major challenge in data mining
Back
Handling missing or incomplete data.
Front
What is text mining
Back
Extracting useful information from text data.
Front
What is the difference between supervised and unsupervised learning
Back
Supervised learning uses labeled data, while unsupervised learning finds hidden patterns in unlabeled data.
Front
What is PCA (Principal Component Analysis)
Back
A dimensionality reduction technique to emphasize variation in a dataset.
Front
What is exploratory data analysis (EDA)
Back
The practice of analyzing datasets visually and statistically to summarize their main characteristics.
Front
What is overfitting in machine learning
Back
A model that performs well on training data but poorly on unseen data.
Front
What is clustering in data mining
Back
A technique to group a set of objects based on their similarities.
Front
What is the Apriori algorithm used for
Back
Mining association rules in datasets.
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This deck focuses on methods and techniques for data mining, analysis, and interpreting datasets to derive meaningful insights.