Crea un piano di cross validation

Ci sono diversi modi per implementare un piano di cross validation a n fold. In questo esercizio creerai un piano di questo tipo usando vtreat::kWayCrossValidation() e lo esaminerai.

kWayCrossValidation() crea un piano di cross validation con la seguente chiamata:

splitPlan <- kWayCrossValidation(nRows, nSplits, dframe, y)

dove nRows è il numero di righe dei dati da suddividere e nSplits è il numero desiderato di fold di cross validation.

A rigor di termini, dframe e y non sono usati da kWayCrossValidation; sono presenti per compatibilità con altre funzioni di partizionamento dei dati di vtreat. Puoi impostarli entrambi a NULL.

Il splitPlan risultante è una lista di nSplits elementi; ciascun elemento contiene due vettori:

train: gli indici di dframe che formeranno il training set
app: gli indici di dframe che formeranno il test set (o application set)

In questo esercizio creerai un piano di cross validation a 3 fold per l'insieme di dati mpg.

Questo esercizio fa parte del corso

Supervised Learning in R: Regression

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Istruzioni dell'esercizio

Carica il pacchetto vtreat.
Ottieni il numero di righe in mpg e assegnalo alla variabile nRows.
Chiama kWayCrossValidation per creare un piano di cross validation a 3 fold e assegnalo alla variabile splitPlan.
- Puoi impostare gli ultimi due argomenti della funzione a NULL.
Chiama str() per esaminare la struttura di splitPlan.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Load the package vtreat
___

# mpg is available
summary(mpg)

# Get the number of rows in mpg
nRows <- ___

# Implement the 3-fold cross-fold plan with vtreat
splitPlan <- ___

# Examine the split plan
___

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: Valutare un modello graficamente Exercise 2: Valuta graficamente il modello sulla disoccupazione Exercise 3: La curva di guadagno per valutare il modello di disoccupazione Exercise 4: Root Mean Squared Error (RMSE)Exercise 5: Calcolare l'RMSE Exercise 6: R-quadro Exercise 7: Calcolare R-squared Exercise 8: Correlazione e R-quadro Exercise 9: Addestrare correttamente un modello Exercise 10: Generare una suddivisione casuale train/test Exercise 11: Addestra un modello usando il train/test split Exercise 12: Valutare un modello con train/test split Exercise 13: Crea un piano di cross validation

Esercizio in corso

Exercise 14: Valutare una procedura di modellazione con la cross-validation a n fold

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