vtreat op de fietsverhuurdata

In deze oefening maak je one-hot-gecodeerde data frames van de juli/augustus-fietsdata, die je later met xgboost gaat gebruiken.

De data frames bikesJuly en bikesAugust zijn al ingeladen.

Voor je gemak hebben we de variabele vars gedefinieerd met de lijst van variabelekolommen voor het model.

Deze oefening maakt deel uit van de cursus

Supervised Learning in R: Regressie

Cursus bekijken

Oefeninstructies

Laad het pakket vtreat.
Gebruik designTreatmentsZ() om een treatment plan treatplan te maken voor de variabelen in vars op basis van bikesJuly (de trainingsdata).
- Zet de vlag verbose=FALSE om te voorkomen dat de functie te veel berichten print.
Vul de lege plekken in om een vector newvars te maken met alleen de namen van de clean- en lev-getransformeerde variabelen. Print deze.
Gebruik prepare() om een one-hot-gecodeerd trainingsdata frame bikesJuly.treat te maken.
- Gebruik het argument varRestrictions om de variabelen te beperken tot newvars.
Gebruik prepare() om op dezelfde manier een one-hot-gecodeerd testframe bikesAugust.treat te maken uit bikesAugust.
Roep str() aan op beide voorbereide testframes om de structuur te bekijken.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# The outcome column
(outcome <- "cnt")

# The input columns
(vars <- c("hr", "holiday", "workingday", "weathersit", "temp", "atemp", "hum", "windspeed"))

# Load the package vtreat
___

# Create the treatment plan from bikesJuly (the training data)
treatplan <- ___(___, ___, verbose = FALSE)

# Get the "clean" and "lev" variables from the scoreFrame
(newvars <- treatplan %>%
  use_series(scoreFrame) %>%        
  filter(code %in% ___) %>%  # get the rows you care about
  use_series(___))           # get the varName column

# Prepare the training data
bikesJuly.treat <- ___(___, ___,  varRestriction = ___)

# Prepare the test data
bikesAugust.treat <- ___(___, ___,  varRestriction = ___)

# Call str() on the treated data
___
___

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Supervised Learning in R: Regressie

SkillTag.level.intermediateSkillTag.label

4.6+

Begin de cursus gratis

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: De intuïtie achter tree-based methoden Exercise 2: Voorspellen met een beslissingsboom Exercise 3: Random forests Exercise 4: Bouw een random forest-model voor fietsverhuur Exercise 5: Voorspel fietsverhuur met het random forest-model Exercise 6: Visualiseer voorspellingen van het random forest-fietsmodel Exercise 7: One-hot-encoding van categorische variabelen Exercise 8: vtreat op een klein voorbeeld Exercise 9: Nieuwe levels Exercise 10: vtreat op de fietsverhuurdata

Huidige oefening

Exercise 11: Gradient boosting-machines Exercise 12: Bepaal het juiste aantal trees voor een gradient boosting machine Exercise 13: Train een xgboost-model voor fietsverhuur en voorspel Exercise 14: Evalueer het xgboost-model voor fietsverhuur Exercise 15: Visualiseer het xgboost-model voor fietsverhuur