DataScience

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Across
  1. 2. The process of interpreting and examining data to extract meaningful insights
  2. 5. Detection Identifying unusual patterns or outliers in data
  3. 7. data variables that represent categories or groups and can take on a limited, fixed number of distinct values
  4. 8. Categorizing data points into predefined classes or groups
  5. 10. An ensemble learning algorithm that combines weak classifiers to create a strong classifier
  6. 12. An error in a model that causes it to consistently predict values away from the true values
  7. 14. Technologies, processes, and tools that help organizations make informed business decisions
  8. 15. Sampling A resampling technique where random samples are drawn with replacement from a dataset
  9. 16. Test A statistical test used to determine if there is a significant association between two categorical variables
  10. 17. Grouping similar data points together based on certain criteria
Down
  1. 1. Data Large and complex datasets that cannot be easily processed using traditional data processing methods
  2. 2. A set of rules that allows one software application to interact with another
  3. 3. A metric that tells us how well a classification model is doing overall, considering different ways of deciding what counts as a positive or negative prediction
  4. 4. Gradient Descent An optimization algorithm that updates model parameters using the entire training dataset (different from mini-batch gradient descent)
  5. 5. A step-by-step set of instructions or rules followed by a computer to solve a problem or perform a task
  6. 6. A statistical method used to analyze the differences among group means in a sample
  7. 9. Testing A statistical method used to compare two versions of a product, webpage, or model to determine which performs better
  8. 11. The measure of how often a classification model correctly predicts outcomes among all instances it evaluates
  9. 12. Classification Categorizing data into two groups, such as spam or not spam
  10. 13. Tradeoff The balance between the error introduced by bias and variance in a model
  11. 15. Statistics A statistical approach that combines prior knowledge with observed data