Data science

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Across
  1. 3. A set of well-defined instructions to solve a particular problem
  2. 7. The model fits exactly against its training data
  3. 8. Fine-tunes mathematical weight functions and improves the model accuracy
  4. 11. Maximum margin
  5. 12. A type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data
Down
  1. 1. Parameters that are explicitly defined to control the learning process
  2. 2. Flow statement for specifying iteration in python
  3. 4. Adding constraint to model parameters
  4. 5. Collection, organization, analysis, interpretation, and presentation of data
  5. 6. Combining the predictions from multiple models
  6. 9. Instance based supervised ML technique
  7. 10. Predict the value of a dependent variable based on the value of another variable