Linear and Logistic Regression

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Across
  1. 1. Technique that splits data into training and test sets
  2. 4. The process of minimizing the cost function using repeated updates
  3. 5. The curve used in logistic regression to map predictions between 0 and 1
  4. 6. A situation where a model performs well on training data but poorly on test data
  5. 13. A regression with more than one input variable
  6. 14. The difference between predicted and actual values
  7. 15. Solving for coefficients analytically using a closed-form solution
  8. 17. Line that best fits the data points in linear regression
  9. 18. Logistic regression is used to solve __________ problems
  10. 20. Function that evaluates how well your model is performing
Down
  1. 2. Parameter that controls the step size in gradient descent
  2. 3. The S-shaped curve used in logistic regression
  3. 7. A measure of how well the regression line approximates the real data points
  4. 8. A technique used to avoid overfitting in logistic regression
  5. 9. The name of the sklearn function used to fit a logistic regression model
  6. 10. Theinput features in a regression model
  7. 11. In regression, these are the weights or slopes learned by the model
  8. 12. Matrix method to solve linear regression without iterations
  9. 16. The variable we are trying to predict
  10. 19. In linear regression, this represents the intercept of the line