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You are working on a spam classification system using regularized logistic regression. "Spam" is a positive class (y = 1)and "not spam" is the negative class (y = 0). You have trained your classifier and there are m = 1000 examples in the cross-validation set. The chart of predicted class vs. actual class is: Actual class: 1 Actual class: 0 Predicted class: 1 85 890 Predicted class: 0 15 10 For reference: Accuracy = (true positives + true negatives) / (total examples) Precision = (true positives) / (true positives + false positives) Recall = (true positives) / (true positives + false negatives) F1 score = (2 * precision * recall) / (precision + recall) What is the classifier's F1 score (as a value from 0 to 1)? Write all steps

You are working on a spam classification system using regularized logistic regression. "Spam" is a positive class (y = 1)and "not spam" is the negative class (y=0). You have trained your classifier and there are m= 1000 examples in the cross-validation set. The chart of predicted class vs. actual class is: Predicted class: 1 Predicted class: 0 Actual class: 1 85 15 For reference: Accuracy = (true positives + true negatives)/(total examples) Precision = (true positives)/(true positives + false positives) Recall = (true positives)/ (true positives + false negatives) F1 score = (2* precision * recall)/(precision + recall) What is the classifier's F1 score (as a value from 0 to 1)? Write all steps Use the editor to format your answer Actual class: 0 890 10

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For evaluation of regression models, typically, the higher the      [ Select ]    ["Adjusted R Squared", "Residual Standard Error"]  the better, and the lower the      [ Select ]    ["Adjusted R Squared", "Residual Standard Error"]  , the better.

The hyper-parameters of a model must NOT be tuned on the test data ( i.e, the data used to evaluate the performance of the final model after selecting the hyper-parameters) Group of answer choices True False

If we add more independent variables into the model:   A. The adjusted R2 value will increase.   B. The R2 value will increase.   C. The R2 value will decrease if the variables we are adding into the model should not be there.   D. The R2 will be biased.

Write the objective function that can be used to determine the regression model parameters. How is this objective function will be used to find model parameters?

In python, for a sample data with 4 columns and 60 rows how do you find the parameters for the regression with the feature map (see attached) where we consider the loss function to be the square of residuals. Once this is done, how do you compute the empirical risk? I've attached some of the data below, it would be sufficient to see how you get results for the question using the above dataset.  1 14 25 620 -1 69 29 625 0 83 27 850 0 28 25 1315 1 41 25 2120 -1 153 31 1315 0 55 25 2600 0 55 31 490 1 69 25 3110 1 83 25 3535

You've completed a regression analysis, but after considering the variables' relationships, you've chosen to swap the explanatory and response variables. You should expect the following after refining the regression model to the data: Group of answer choices a. the value of the correlation coefficient will change b. the value of SSE will change c. the sign of the slope will change d. the value of the coefficient of determination will change

The metrics that are calculated for the training set measures the goodness of fit of the fitted model and those calculated for the validation set are used to measure predictive performance. Training errors are expected to be smaller than validation errors. A smaller difference between the training and validation errors is the likelihood of overfitting the training data. True False

Which qualities are preferred for an estimator? (Select all that apply.) Not centered around the unknown parameter Variability is small Centered around the unknown parameter Variability is large