Modellare un'interazione

In questo esercizio userai le interazioni per modellare l'effetto del sesso e dell'attività gastrica sul metabolismo dell'alcol.

Il data frame alcohol è già caricato e contiene le colonne:

Metabol: la velocità di metabolismo dell'alcol
Gastric: il tasso di attività della alcol deidrogenasi gastrica
Sex: il sesso della persona che beve (Male o Female)

Nel video abbiamo adattato tre modelli ai dati alcohol:

uno con soli termini additivi (effetti principali): Metabol ~ Gastric + Sex
due modelli, ciascuno con interazioni tra attività gastrica e sesso

Hai visto che uno dei modelli con termini di interazione aveva un R-squared migliore rispetto al modello additivo, suggerendo che usare termini di interazione offre un adattamento migliore. In questo esercizio confronterai l'R-squared di uno dei modelli con interazione con quello del modello con soli effetti principali.

Ricorda che l'operatore : indica l'interazione tra due variabili. L'operatore * indica l'interazione tra le due variabili, più gli effetti principali.

x*y = x + y + x:y

Questo esercizio fa parte del corso

Supervised Learning in R: Regression

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Istruzioni dell'esercizio

Scrivi una formula che esprima Metabol come funzione di Gastric e Sex senza interazioni.
- Assegna la formula alla variabile fmla_add e stampala.
Scrivi una formula che esprima Metabol come funzione dell'interazione tra Gastric e Sex.
- Aggiungi Gastric come effetto principale, ma non Sex.
- Assegna la formula alla variabile fmla_interaction e stampala.
Adatta un modello lineare con soli effetti principali: model_add ai dati.
Adatta un modello lineare con l'interazione: model_interaction ai dati.
Chiama summary() su entrambi i modelli. Quale ha un R-squared migliore?

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# alcohol is available
summary(alcohol)

# Create the formula with main effects only
(fmla_add <- ___ )

# Create the formula with interactions
(fmla_interaction <- ___ )

# Fit the main effects only model
model_add <- ___

# Fit the interaction model
model_interaction <- ___

# Call summary on both models and compare
___
___

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: Variabili categoriche in input Exercise 2: Esaminare la struttura degli input categorici Exercise 3: Modellare con input categorici Exercise 4: Interazioni Exercise 5: Modellare un'interazione

Esercizio in corso

Exercise 6: Modeling an interaction (2)Exercise 7: Trasformare la risposta prima della modellazione Exercise 8: Errore relativo Exercise 9: Modellare un output monetario trasformato con log Exercise 10: Confrontare RMSE e root-mean-squared Relative Error Exercise 11: Trasformare gli input prima della modellazione Exercise 12: Trasformazioni degli input: la "mazza da hockey"Exercise 13: Trasformazioni degli input: la "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