Random Forest: visualization

Now you need to plot the predictions. With the gradient boosted trees model, you drew a scatter plot of predicted responses vs. actual responses, and a density plot of the residuals. You are now going to adapt those plots to display the results from both models at once.

Bu egzersiz

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kursunun bir parçasıdır

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Egzersiz talimatları

A local tibble both_responses, containing predicted and actual years for both models, has been pre-defined.

Update the predicted vs. actual response scatter plot.
- Use the both_responses dataset.
- Add a color aesthetic to draw each model in a different color. Use color = model.
- Rather than drawing the points, use geom_smooth() to draw a smooth curve for each model.
Create a tibble of residuals, named residuals.
- Call mutate() on both_responses.
- The new column should be called residual.
- residual should be equal to the predicted response minus the actual response.
Update the residual density plot.
- Add a color aesthetic to draw each model in a different color.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# both_responses has been pre-defined
both_responses

# Draw a scatterplot of predicted vs. actual
ggplot(___, aes(actual, predicted, ___)) +
  # Add a smoothed line
  ___ +
  # Add a line at actual = predicted
  geom_abline(intercept = 0, slope = 1)

# Create a tibble of residuals
residuals <- ___

# Draw a density plot of residuals
ggplot(residuals, aes(residual, ___)) +
    # Add a density curve
    geom_density() +
    # Add a vertical line through zero
    geom_vline(xintercept = 0)

Kodu Düzenle ve Çalıştır