Which of the following evaluation metrics can be used to evaluate a model with categorical output variable with more than two categories? (check tree best answers) ☐ sensitivity (true positive rate, recall) deviance cross-entropy O accuracy (proportion of correctly predicted outputs) AUC and ROC root mean squared error ☐ specificity (true negative rate) ☐ false positive rate

The lower the misclassification rate, the better the predictive performance of a logistic regression model. True False PLEASE ANSWER CORRECTLY

Which statement about k-fold cross-validation is FALSE?   Group of answer choices is typically used to tune and select the best hyper-parameters for the model On each step, one fold is used  as the training data and the remaining k − 1 folds are used as testing data partitions the data into k non-overlapping folds The last step of the k-fold cross-validation is to compute the average performance estimate All observations are used for both training and validation

PLEASE ANSWER IT FAST AND BE CORRECT with explanation , I WILL RATE FULLY   What does the CLI option do on the MODEL statement of an MLR analysis in PROC GLM? a. Produce prediction intervals for the slope parameters. b. Produce confidence intervals for the slope parameters. c. Produce confidence intervals for the mean response at all predictor combinations in the dataset. d. Produce prediction intervals for a future response at all predictor combinations in the dataset.

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Make sure to show it as steps on a python with the final answer Fit a decision tree model using the training dataset (`x_train` and `y_train`)Create a variable named `y_pred`. Make predictions using the `x_test` variable and save these predictions to the y_pred variable Create a variable called `dt_accuracy`. Compute the accuracy rate of the logistic regression model using the `y_pred` and `y_test` and assign it to the `dt_accuracy` variable

Question # 1: Consider the following data of Regression Model where YACTUAL  is your actual observation and YPREDICTION is the model prediction value. You have to use the data and Compute the value of the following errors: Mean Absolute Error, Mean Relative Error and Prediction (X) when X ≥ 30% Value of R2 YACTUAL 12.6 9.8 9.6 9.9 11.5 11.2 12.3 9.5 9.7 12.4 YPREDICTED 12.8 9.1 9.7 9.9 11.3 12.1 12.0 9.8 9.6 12.2

Which of the following model should be used to show a single outcome with quantitative input values has no randomness? a. The threshold model O b. The deterministic model O. The statistical model d. The stochastic model

You have trained a logistic regression classifier and planned to make predictions according to: Predict y=1 if ho(x) 2 threshold Predict y=0 if ho (x) < threshold For different threshold values, you get different values of precision (P) and recall (R). Which of the following is a reasonable way to pick the threshold value? O a Measure precision (P) and recall (R) on the test set and choose the value of P+R threshold which maximizes 2 Ob Measure precision (P) and recall (R) on the cross validation set and choose the P+R value of threshold which maximizes 2 Measure precision (P) and recall (R) on the cross validation set and choose the PR value of threshold which maximizes 2 P+R Measure precision (P) and recall (R) on the test set and choose the value of PR threshold which maximizes 2 P+R

Gradient Descent algorithm