Train een regressieboom

Zoals je al weet, zijn beslisbomen een handig hulpmiddel voor classificatieproblemen. Daarnaast kun je ze ook gebruiken om regressieproblemen te modelleren. Het structurele verschil is dat er numerieke waarden (in plaats van klassen) op de bladknopen staan.

In deze oefening gebruik je de chocolate-gegevensset om een regressieboom te fitten. Dit lijkt sterk op wat je al in hoofdstuk 1 deed met de diabetes-gegevensset.

De trainingsdata chocolate_train zijn beschikbaar in je werkruimte.

Deze oefening maakt deel uit van de cursus

Machine Learning met boomgebaseerde modellen in R

Cursus bekijken

Oefeninstructies

Bouw model_spec, een specificatie voor een regressieboom.
Gebruik op basis van de data frame chocolate_train model_spec om een regressieboom te trainen die final_grade voorspelt met alleen de numerieke predictors in de data.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

library(tidymodels)

# Build the specification
model_spec <- decision_tree() %>%
  set_mode(___) %>%
  set_engine(___)

# Fit to the data
model_fit <- model_spec %>%
  ___(formula = ___,
      data = ___)

model_fit

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Machine Learning met boomgebaseerde modellen in R

SkillTag.level.beginnerSkillTag.label

4.9+

Begin de cursus gratis

Ready to build a real machine learning pipeline? Complete step-by-step exercises to learn how to create decision trees, split your data, and predict which patients are most likely to suffer from diabetes. Last but not least, you’ll build performance measures to assess your models and judge your predictions.

Exercise 1: Welcome to the course!Exercise 2: Why tree-based methods?Exercise 3: Specify that tree Exercise 4: Train that model Exercise 5: How to grow your tree Exercise 6: Train/test split Exercise 7: Avoiding class imbalances Exercise 8: From zero to hero Exercise 9: Predict and evaluate Exercise 10: Make predictions Exercise 11: Crack the matrix Exercise 12: Are you predicting correctly?

Ready for some candy? Use a chocolate rating dataset to build regression trees and assess their performance using suitable error measures. You’ll overcome statistical insecurities of single train/test splits by applying sweet techniques like cross-validation and then dive even deeper by mastering the bias-variance tradeoff.

Exercise 1: Continue uitkomsten Exercise 2: Train een regressieboom

Huidige oefening

Exercise 3: Nieuwe waarden voorspellen Exercise 4: Inspecteer de modeloutput Exercise 5: Prestatiemetrieken voor regressiebomen Exercise 6: Prestatie binnen de trainingsset Exercise 7: Out-of-sample-prestatie Exercise 8: Grotere fouten, zwaardere straf Exercise 9: Cross-validatie Exercise 10: Maak de folds Exercise 11: De folds fitten Exercise 12: Evalueer de folds Exercise 13: Afweging tussen bias en variantie Exercise 14: Noem dingen bij hun naam Exercise 15: Modelcomplexiteit aanpassen Exercise 16: Prestaties in-sample en out-of-sample

Time to get serious with tuning your hyperparameters and interpreting receiver operating characteristic (ROC) curves. In this chapter, you’ll leverage the wisdom of the crowd with ensemble models like bagging or random forests and build ensembles that forecast which credit card customers are most likely to churn.

Exercise 1: Tuning hyperparameters Exercise 2: Generate a tuning grid Exercise 3: Tune along the grid Exercise 4: Pick the winner Exercise 5: More model measures Exercise 6: Calculate specificity Exercise 7: Draw the ROC curve Exercise 8: Area under the ROC curve Exercise 9: Bagged trees Exercise 10: Create bagged trees Exercise 11: In-sample ROC and AUC Exercise 12: Check for overfitting Exercise 13: Random forest Exercise 14: Bagged trees vs. random forest Exercise 15: Variable importance

Ready for the high society of tree-based models? Apply gradient boosting to create powerful ensembles that perform better than anything that you have seen or built. Learn about their fine-tuning and how to compare different models to pick a winner for production.

Exercise 1: Introduction to boosting Exercise 2: Bagging vs. boosting Exercise 3: Specify a boosted ensemble Exercise 4: Gradient boosting Exercise 5: Train a boosted ensemble Exercise 6: Evaluate the ensemble Exercise 7: Compare to a single classifier Exercise 8: Optimize the boosted ensemble Exercise 9: Tuning preparation Exercise 10: The actual tuning Exercise 11: Finalize the model Exercise 12: Model comparison Exercise 13: Compare AUC Exercise 14: Plot ROC curves Exercise 15: Wrap-up