Modelleren met categorische inputs

In deze oefening pas je een lineair model toe op de flowers-data om Flowers te voorspellen op basis van Time en Intensity.

De modelformule fmla die je in de vorige oefening hebt gemaakt is nog beschikbaar, net als de modelmatrix mmat.

Deze oefening maakt deel uit van de cursus

Supervised Learning in R: Regressie

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Oefeninstructies

Gebruik fmla en lm om een lineair model te trainen dat Flowers voorspelt op basis van Intensity en Time. Ken het model toe aan de variabele flower_model.
Gebruik summary() om jezelf te herinneren aan de structuur van mmat.
Gebruik summary() om flower_model te onderzoeken. Komen de variabelen overeen met wat je in mmat zag?
Gebruik flower_model om het aantal bloemen te voorspellen. Voeg de voorspellingen toe aan flowers als de kolom predictions.
Vul de lege plekken in om voorspellingen vs. werkelijke bloemen te plotten (voorspellingen op de x-as).

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# flowers is available
str(flowers)

# fmla is available
fmla

# Fit a model to predict Flowers from Intensity and Time : flower_model
flower_model <- ___

# Use summary on mmat to remind yourself of its structure
___

# Use summary to examine flower_model 
___

# Predict the number of flowers on each plant
flowers$predictions <- ___

# Plot predictions vs actual flowers (predictions on x-axis)
ggplot(___, aes(x = ___, y = ___)) + 
  geom_point() +
  geom_abline(color = "blue")

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Supervised Learning in R: Regressie

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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: Categorische invoervariabelen Exercise 2: De structuur van categorische input verkennen Exercise 3: Modelleren met categorische inputs

Huidige oefening

Exercise 4: Interacties Exercise 5: Een interactie modelleren 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