Which of the following evaluation metrics can be used to evaluate a model with continuous output variable? (check all that apply) ☐ specificity (true negative rate) mean squared error accuracy rate ☐ sensitivity (true positive rate, recall) root mean squared error ROC curve
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A: Answer: Because R2 always increases when variables are added to the model
Q: ry (https://archive.ics.uci.edu/ml/datasets.php) and identify two data sets, one for…
A: as per question the solution is an given below :
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A: According to the information given:- We have to choose the correct option to satisfy the statement.
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A: Introduction: Predictive modeling is a statistical process of creating a mathematical model to…
Q: What are some applications of linear regression?
A: - We need to have some of the applications of the linear regression.
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A: “Since you have posted multiple questions, we will provide the solution only to the first question…
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A: Epoch meaning:- An epoch is a term used in machine learning and indicates the number of passes of…
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A: Training data is the initial data used to train machine learning models.
Q: Write the objective function that can be used to determine the regression model parameters. How is…
A: The solution to the given question is: The objective function is the sum of squared errors (SSE).…
Q: How many parameters can be automatically tuned in Linear Regression with Elastic Net Regularization?…
A: Given: To choose the correct option.
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A: I apologize, but I am an AI language model and I do not have access to the Demographics data.…
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A: Here we have given a brief note on the true statements.. you can find them in step 2.
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A: Logistic regression is the statistical and machine learning model used for binary classification…
Q: Which of the following evaluation metrics can not be applied in case of logistic regression output…
A: Solution: Given, Which of the following evaluation metrics can not be applied in case of…
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Q: Which of the following evaluation metrics can be used to evaluate a model with categorical output…
A: In the context of machine learning, a model with a categorical output variable with exactly two…
Q: Describe the different between Outcome Metrics and Driver Metrics. Give an example of Outcome…
A: Ans -- Outcome metrics are the specific data we collect to access the extent to which the expected…
Q: Which statement about k-fold cross-validation is FALSE? Group of answer choices is typically used…
A: Correct Answer Option-B) On each step, one fold is used as the training data and the remaining k -…
Q: R2 over the training sample is 70% and the out-of-sample R2 over the test sample only - 30%. (select…
A: Input of 15 and finding average of positive numbers and summation of negative number
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- 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+RGradient Descent algorithmWhy is RMSE generally the preferred performance measure for regression tasks? A. Because it gives an idea of the average percentage deviation of the predictions from the actual values. B. Because it gives an idea of the average absolute deviation of the predictions from the actual values. C. Because it gives an idea of how much error the system typically makes in its predictions, with a higher weight given to large errors. D. Because it gives an idea of the average deviation of the predictions from the actual values.
- Why use LASSO shrinkage methods for linear regression? Select ALL that are correct. They allow for greater model interpretability. They increase the prediction accuracy of linear regression on training data. They slightly decrease bias and can substantially decrease the variance of the model. They slightly increase bias, but can substantially decrease the variance of the model.You decide to run a simpler model to predict churn, using only the variables tenure (in months) and TotalCharges (in US$). The output is given below. The AIC of this model is 4727.6 (in contrast to the AIC of 4240 for the full model). On the basis of this which model would be expected to give superior predictive performance? Actual ## Coefficients: ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) 2.471e-01 5.360e-02 4.611 4.01e-06 *** ## tenure < 2e-16 *** -1.124e-01 5.816e-03 -19.334 ## TotalCharges 8.236e-04 5.618e-05 14.660 < 2e-16 *** ## No --- ## Signif. codes: 0 ## Yes Yes ## Null deviance: 5701.5 on 4921 ## Residual deviance: 4721.6 on 4919 ## AIC: 4727.6 515 345 ## (Dispersion parameter for binomial family taken to be 1) ## Predicted ***** No 795 3267 0.001 Confusion Matrix (Training) **** Actual 0.01 Yes No degrees of freedom degrees of freedom Yes The simpler model (with just tenure and TotalCharges) The full model (with all variables) 0.05 0.1 220 145 Predicted No 339…We've built a logistic regression model in RapidMiner, and would like to use it to make predictions for some new data points. Which operator do we need: Performance Apply Model Cross Validation O Nominal to Numerical.
- You are given the true labels and the predicted probabilities from logistic regression model for N test examples. Approximately compute the AUC scores for ROC and PR curves.The predictive performance of a model is the measure of how close the model’s prediction values are to the actual values. A close-to-ideal model would have the minimum error in the predicted and actual values. The validation set is used to assess the predictive ability of the model which has been trained using the training set. True False