Fit an xgboost bike rental model and predict

In this exercise, you will fit a gradient boosting model using xgboost() to predict the number of bikes rented in an hour as a function of the weather and the type and time of day. You will train the model on data from the month of July and predict on data for the month of August.

The data frames bikesJuly, bikesJuly.treat, bikesAugust, and bikesAugust.treat have also been pre-loaded. Remember the vtreat-ed data no longer has the outcome column, so you must get it from the original data (the cnt column).

For convenience, the number of trees to use, ntrees from the previous exercise is available to use.

The arguments to xgboost() (docs) are similar to those of xgb.cv().

Diese Übung ist Teil des Kurses

Supervised Learning in R: Regression

Anleitung zur Übung

Fill in the blanks to run xgboost() on the July data.
- Use as.matrix() to convert the vtreated data frame to a matrix.
- The objective should be "reg:squarederror".
- Use ntrees rounds.
- Set eta to 0.75, max_depth to 5, and verbose to FALSE (silent).
Now call predict() on bikesAugust.treat to predict the number of bikes rented in August.
- Use as.matrix() to convert the vtreat-ed test data into a matrix.
- Add the predictions tobikesAugust as the column pred.
Fill in the blanks to plot actual bike rental counts versus the predictions (predictions on the x-axis).
- Do you see a possible problem with the predictions?

Interaktive Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Run xgboost
bike_model_xgb <- xgboost(data = ___, # training data as matrix
                   label = ___,  # column of outcomes
                   nrounds = ___,       # number of trees to build
                   objective = ___, # objective
                   eta = ___,
                   max_depth = ___,
                   verbose = FALSE  # silent
)

# Make predictions
bikesAugust$pred <- ___(___, ___(___))

# Plot predictions (on x axis) vs actual bike rental count
ggplot(bikesAugust, aes(x = ___, y = ___)) + 
  geom_point() + 
  geom_abline()

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Diese Übung ist Teil des Kurses

Supervised Learning in R: Regression

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In this chapter we introduce the concept of regression from a machine learning point of view. We will present the fundamental regression method: linear regression. We will show how to fit a linear regression model and to make predictions from the model.

Exercise 1: Welcome and Introduction Exercise 2: Identify the regression tasks Exercise 3: Linear regression - the fundamental method Exercise 4: Code a simple one-variable regression Exercise 5: Examining a model Exercise 6: Predicting once you fit a model Exercise 7: Predicting from the unemployment model Exercise 8: Multivariate linear regression (Part 1)Exercise 9: Multivariate linear regression (Part 2)Exercise 10: Wrapping up linear regression

Now that we have learned how to fit basic linear regression models, we will learn how to evaluate how well our models perform. We will review evaluating a model graphically, and look at two basic metrics for regression models. We will also learn how to train a model that will perform well in the wild, not just on training data. Although we will demonstrate these techniques using linear regression, all these concepts apply to models fit with any regression algorithm.

Exercise 1: Evaluating a model graphically Exercise 2: Graphically evaluate the unemployment model Exercise 3: The gain curve to evaluate the unemployment model Exercise 4: Root Mean Squared Error (RMSE)Exercise 5: Calculate RMSE Exercise 6: R-squared Exercise 7: Calculate R-squared Exercise 8: Correlation and R-squared Exercise 9: Properly Training a Model Exercise 10: Generating a random test/train split Exercise 11: Train a model using test/train split Exercise 12: Evaluate a model using test/train split Exercise 13: Create a cross validation plan Exercise 14: Evaluate a modeling procedure using n-fold cross-validation

Before moving on to more sophisticated regression techniques, we will look at some other modeling issues: modeling with categorical inputs, interactions between variables, and when you might consider transforming inputs and outputs before modeling. While more sophisticated regression techniques manage some of these issues automatically, it's important to be aware of them, in order to understand which methods best handle various issues -- and which issues you must still manage yourself.

Exercise 1: Categorical inputs Exercise 2: Examining the structure of categorical inputs Exercise 3: Modeling with categorical inputs Exercise 4: Interactions Exercise 5: Modeling an interaction Exercise 6: Modeling an interaction (2)Exercise 7: Transforming the response before modeling Exercise 8: Relative error Exercise 9: Modeling log-transformed monetary output Exercise 10: Comparing RMSE and root-mean-squared Relative Error Exercise 11: Transforming inputs before modeling Exercise 12: Input transforms: the "hockey stick"Exercise 13: Input transforms: the "hockey stick" (2)

Now that we have mastered linear models, we will begin to look at techniques for modeling situations that don't meet the assumptions of linearity. This includes predicting probabilities and frequencies (values bounded between 0 and 1); predicting counts (nonnegative integer values, and associated rates); and responses that have a non-linear but additive relationship to the inputs. These algorithms are variations on the standard linear model.

Exercise 1: Logistic regression to predict probabilities Exercise 2: Fit a model of sparrow survival probability Exercise 3: Predict sparrow survival Exercise 4: Poisson and quasipoisson regression to predict counts Exercise 5: Poisson or quasipoisson Exercise 6: Fit a model to predict bike rental counts Exercise 7: Predict bike rentals on new data Exercise 8: Visualize the bike rental predictions Exercise 9: GAM to learn non-linear transforms Exercise 10: Writing formulas for GAM models Exercise 11: Writing formulas for GAM models (2)Exercise 12: Model soybean growth with GAM Exercise 13: Predict with the soybean model on test data

In this chapter we will look at modeling algorithms that do not assume linearity or additivity, and that can learn limited types of interactions among input variables. These algorithms are *tree-based* methods that work by combining ensembles of *decision trees* that are learned from the training data.

Exercise 1: The intuition behind tree-based methods Exercise 2: Predicting with a decision tree Exercise 3: Random forests Exercise 4: Build a random forest model for bike rentals Exercise 5: Predict bike rentals with the random forest model Exercise 6: Visualize random forest bike model predictions Exercise 7: One-Hot-Encoding Categorical Variables Exercise 8: vtreat on a small example Exercise 9: Novel levels Exercise 10: vtreat the bike rental data Exercise 11: Gradient boosting machines Exercise 12: Find the right number of trees for a gradient boosting machine Exercise 13: Fit an xgboost bike rental model and predict

Aktuelle Übung

Exercise 14: Evaluate the xgboost bike rental model Exercise 15: Visualize the xgboost bike rental model