Classification evaluation

In the previous lesson, you have built a logistic model to predict Parkinson's disease. In this exercise, you will compare predictions against actual values.

This skill is vital because companies focus on the results. Employers want to know how accurate the models that you develop are.

Once again, you will build a model to predict the status of Parkinson's disease. This time, you will build the model on part of the dataset and use the rest for testing.

Remember that \(\text{recall} = \frac{TP}{TP+FN}\).

\(TP\) means that we have predicted a positive value and we were right.

\(FN\) means that we have predicted a negative value but we were wrong.

Around 80% of the rows of the parkinsons dataset have been assigned to train, and the rest have been assigned to test.

This exercise is part of the course

Practicing Statistics Interview Questions in R

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Build a logistic model on the train data
model <- ___(___ ~ NHR + ___, data = ___, family = ___)

# Calculate probabilities for the test data
probabilities <- ___(___, newdata = ___, type = ___)

Edit and Run Code

This exercise is part of the course

Practicing Statistics Interview Questions in R

AdvancedSkill Level

4.7+

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Want to increase your odds of acing your job interview? If so, brush up on your knowledge of probability theory. In this chapter, we'll roll dice and shoot baskets to explain probabilities using real-life examples.

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Exercise 1: Normality tests Exercise 2: Shapiro-Wilk test Exercise 3: Q-Q plot Exercise 4: Inference for a mean Exercise 5: Confidence interval Exercise 6: One-sample t-test Exercise 7: Comparing two means Exercise 8: Two-sample t-test Exercise 9: Paired test Exercise 10: ANOVA Exercise 11: Comparing groups Exercise 12: ANOVA for plant growth

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Exercise 1: Covariance and correlation Exercise 2: Covariance by hand Exercise 3: Linear relationship Exercise 4: Nonlinear relationship Exercise 5: Linear regression model Exercise 6: Fitting linear models Exercise 7: Predicting with linear models Exercise 8: Logistic regression model Exercise 9: Fitting logistic models Exercise 10: Predicting with logistic models Exercise 11: Model evaluation Exercise 12: Validation set approach Exercise 13: Regression evaluation Exercise 14: Classification evaluation

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Exercise 15: Wrapping up