Generare una suddivisione casuale train/test

Nei prossimi esercizi userai i dati mpg del pacchetto ggplot2. Questi dati descrivono le caratteristiche di diversi marchi e modelli di auto in anni differenti. L’obiettivo è prevedere l’efficienza in città a partire dall’efficienza in autostrada.

In questo esercizio, dividerai mpg in un insieme di training mpg_train (75% dei dati) e un insieme di test mpg_test (25% dei dati). Un modo per farlo è generare una colonna di numeri casuali uniformi tra 0 e 1 usando la funzione runif() (docs).

Se hai un insieme di dati dframe di dimensione \(N\) e vuoi un sottoinsieme casuale di dimensione approssimativa pari al \(100 * X\)% di \(N\) (dove \(X\) è tra 0 e 1), allora:

Genera un vettore di numeri casuali uniformi: gp = runif(N).
dframe[gp < X,] avrà all’incirca la dimensione desiderata.
dframe[gp >= X,] sarà il complemento.

Questo esercizio fa parte del corso

Supervised Learning in R: Regression

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Istruzioni dell'esercizio

Usa la funzione nrow (docs) per ottenere il numero di righe del data frame mpg. Assegna questo conteggio alla variabile N e stampalo.
Calcola quante righe corrispondono circa al 75% di N. Assegnale alla variabile target e stampala.
Usa runif() per generare un vettore di N numeri casuali uniformi, chiamato gp.
Usa gp per dividere mpg in mpg_train e mpg_test (con mpg_train che contiene circa il 75% dei dati).
Usa nrow() per controllare la dimensione di mpg_train e mpg_test. Sono circa della dimensione giusta?

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# mpg is available
summary(mpg)
dim(mpg)

# Use nrow to get the number of rows in mpg (N) and print it
(N <- ___)

# Calculate how many rows 75% of N should be and print it
# Hint: use round() to get an integer
(target <- ___)

# Create the vector of N uniform random variables: gp
gp <- ___

# Use gp to create the training set: mpg_train (75% of data) and mpg_test (25% of data)
mpg_train <- ___
mpg_test <- ___

# Use nrow() to examine mpg_train and mpg_test
___
___

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

Esercizio in corso

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 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