Een interactie modelleren

In deze oefening gebruik je interacties om het effect van geslacht en maagactiviteit op alcoholmetabolisme te modelleren.

De alcohol-dataframe is vooraf geladen en bevat de kolommen:

Metabol: de snelheid van alcoholmetabolisme
Gastric: de activiteitssnelheid van maag-alcoholdehydrogenase
Sex: het geslacht van de drinker (Male of Female)

In de video hebben we drie modellen op de alcohol-gegevens gefit:

één met alleen additieve (hoofdeffect) termen: Metabol ~ Gastric + Sex
twee modellen, elk met interacties tussen maagactiviteit en geslacht

Je zag dat een van de modellen met interactietermen een hogere R-squared had dan het additieve model, wat suggereert dat interactietermen een betere fit geven. In deze oefening vergelijk je de R-squared van een van de interactiemodellen met het model met alleen hoofdeffecten.

Onthoud dat de operator : de interactie tussen twee variabelen aangeeft. De operator * geeft de interactie tussen de twee variabelen aan, plus de hoofdeffecten.

x*y = x + y + x:y

Deze oefening maakt deel uit van de cursus

Supervised Learning in R: Regressie

Cursus bekijken

Oefeninstructies

Schrijf een formule die Metabol uitdrukt als functie van Gastric en Sex zonder interacties.
- Wijs de formule toe aan de variabele fmla_add en print die.
Schrijf een formule die Metabol uitdrukt als functie van de interactie tussen Gastric en Sex.
- Voeg Gastric toe als hoofdeffect, maar niet Sex.
- Wijs de formule toe aan de variabele fmla_interaction en print die.
Fit een lineair model met alleen hoofdeffecten: model_add op de gegevens.
Fit een lineair model met de interactie: model_interaction op de gegevens.
Roep summary() aan op beide modellen. Welke heeft een betere R-squared?

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# 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
___
___

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: Categorische invoervariabelen Exercise 2: De structuur van categorische input verkennen Exercise 3: Modelleren met categorische inputs Exercise 4: Interacties Exercise 5: Een interactie modelleren

Huidige oefening

Exercise 6: Een interactie modelleren (2)Exercise 7: De respons transformeren vóór het modelleren Exercise 8: Relatieve fout Exercise 9: Modeleren met log-getransformeerde monetaire output Exercise 10: RMSE vergelijken met root-mean-squared relatieve fout Exercise 11: Invoer transformeren vóór het modelleren Exercise 12: Inputtransformaties: de "hockeystick"Exercise 13: Inputtransformaties: de "hockeystick" (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