Visualizza il modello xgboost per il noleggio bici

Hai visto tre modi diversi per modellare i dati di noleggio bici. In questo esempio, hai visto che il modello di gradient boosting ha avuto il RMSE più basso. Per concludere il corso, confrontiamo le previsioni del modello di gradient boosting con gli altri due modelli in funzione del tempo.

Completando questo esercizio, avrai terminato il corso. Congratulazioni! Ora hai gli strumenti per applicare varie strategie ai tuoi compiti di regressione.

Il data frame bikesAugust con le previsioni è già caricato. I grafici quasipoisson_plot e randomforest_plot sono anche disponibili.

Questo esercizio fa parte del corso

Supervised Learning in R: Regression

Visualizza il corso

Istruzioni dell'esercizio

Stampa quasipoisson_plot per rivedere il comportamento del modello quasi-poisson.
Stampa randomforest_plot per rivedere il comportamento del modello random forest.
Compila gli spazi vuoti per tracciare le previsioni del gradient boosting e i conteggi effettivi per ora per i primi 14 giorni di agosto.
- Usa pivot_longer() per trasformare i nomi delle colonne cnt e gbm in una colonna chiamata value, con una chiave chiamata valuetype.
- Traccia value in funzione di instant (giorno).

Come si confronta il modello di gradient boosting con i modelli precedenti?

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Print quasipoisson_plot
___

# Print randomforest_plot
___

# Plot predictions and actual bike rentals as a function of time (days)
bikesAugust %>% 
  mutate(instant = (instant - min(instant))/24) %>%  # set start to 0, convert unit to days
  filter(instant < 14) %>% # first two weeks
  pivot_longer(c(___, ___), names_to = ___, values_to = ___) %>%
  ggplot(aes(x = ___, y = ___, color = valuetype, linetype = valuetype)) + 
  geom_point() + 
  geom_line() + 
  scale_x_continuous("Day", breaks = 0:14, labels = 0:14) + 
  scale_color_brewer(palette = "Dark2") + 
  ggtitle("Predicted August bike rentals, Gradient Boosting model")

Modifica ed esegui il codice

Questo esercizio fa parte del corso

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: L'intuizione dietro i metodi basati su alberi Exercise 2: Prevedere con un albero di decisione Exercise 3: Random forest Exercise 4: Crea un modello Random Forest per i noleggi di biciclette Exercise 5: Prevedi i noleggi di biciclette con il modello random forest Exercise 6: Visualizza le previsioni del modello random forest per le bici Exercise 7: One-Hot Encoding delle variabili categoriche Exercise 8: vtreat su un piccolo esempio Exercise 9: Livelli inediti Exercise 10: Usa vtreat sui dati di noleggio bici Exercise 11: Gradient boosting machines Exercise 12: Trova il numero giusto di alberi per un gradient boosting machine Exercise 13: Stima un modello xgboost per il noleggio bici e prevedi Exercise 14: Valuta il modello xgboost per il noleggio bici Exercise 15: Visualizza il modello xgboost per il noleggio bici

Esercizio in corso