Wat zijn de coëfficiënten?

Om een goed gevoel te krijgen voor het verschil tussen gefitte modelparameters en hyperparameters, kijken we van dichterbij naar die gefitte parameters: in ons eenvoudige lineaire model de coëfficiënten. De gegevensset breast_cancer_data is al voor je geladen en het lineaire model is aangeroepen zoals in de vorige les, dus je hebt rechtstreeks toegang tot het object linear_model.

In ons lineaire model kun je de coëfficiënten als volgt opvragen: linear_model$coefficients. En we kunnen de relatie die we gemodelleerd hebben visualiseren met een plot.

Onthoud dat een lineair model de basisformule heeft: y = x * slope + intercept

Deze oefening maakt deel uit van de cursus

Hyperparameterafstelling in R

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Oefeninstructies

Verken de coëfficiënten van het linear_model in de console.
Plot de regressielijn met ggplot2.
Ken de juiste coëfficiënten toe aan slope en intercept.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

library(ggplot2)

# Plot linear relationship.
ggplot(data = breast_cancer_data, 
        aes(x = symmetry_mean, y = concavity_mean)) +
  geom_point(color = "grey") +
  ___(slope = ___$___[___], 
      intercept = ___$___[___])

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Hyperparameterafstelling in R

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4.8+

Begin de cursus gratis

Why do we use the strange word "hyperparameter"? What makes it hyper? Here, you will understand what model parameters are, and why they are different from hyperparameters in machine learning. You will then see why we would want to tune them and how the default setting of caret automatically includes hyperparameter tuning.

Exercise 1: Parameters vs. hyperparameters Exercise 2: Modelparameters vs. hyperparameters Exercise 3: Hyperparameters in lineaire modellen Exercise 4: Wat zijn de coëfficiënten?

Huidige oefening

Exercise 5: Herhaling van de basis van Machine Learning Exercise 6: Machine learning met caret Exercise 7: Resampling-schema's Exercise 8: Hyperparametertuning in caret Exercise 9: Hyperparameters in Stochastic Gradient Boosting Exercise 10: Het aantal te tunen hyperparameters aanpassen Exercise 11: Hyperparameters handmatig afstellen

In this chapter, you will learn how to tune hyperparameters with a Cartesian grid. Then, you will implement faster and more efficient approaches. You will use Random Search and adaptive resampling to tune the parameter grid, in a way that concentrates on values in the neighborhood of the optimal settings.

Exercise 1: Hyperparameter tuning in caret Exercise 2: Finding hyperparameters Exercise 3: Cartesian grid search in caret Exercise 4: Plot hyperparameter model output Exercise 5: Grid vs. Random Search Exercise 6: Grid search with range of hyperparameters Exercise 7: Find train() option for random search Exercise 8: Random search with caret Exercise 9: Adaptive resampling Exercise 10: Advantages of Adaptive Resampling Exercise 11: Adaptive Resampling with caret

Here, you will use another package for machine learning that has very convenient hyperparameter tuning functions. You will define a Cartesian grid or perform Random Search, as well as advanced techniques. You will also learn different ways to plot and evaluate models with different hyperparameters.

Exercise 1: Machine learning with mlr Exercise 2: Machine Learning with mlr Exercise 3: Modeling with mlr Exercise 4: Grid and random search with mlr Exercise 5: Random search with mlr Exercise 6: Perform hyperparameter tuning with mlr Exercise 7: Evaluating hyperparameters with mlr Exercise 8: Why to evaluate tuning?Exercise 9: Evaluating hyperparameter tuning results Exercise 10: Advanced tuning with mlr Exercise 11: Define advanced tuning controls Exercise 12: Define aggregated measures Exercise 13: Setting hyperparameters

In this final chapter, you will use h2o, another package for machine learning with very convenient hyperparameter tuning functions. You will use it to train different models and define a Cartesian grid. Then, You will implement a Random Search use stopping criteria. Finally, you will learn AutoML, an h2o interface which allows for very fast and convenient model and hyperparameter tuning with just one function.

Exercise 1: Machine learning with h2o Exercise 2: Prepare data for modelling with h2o Exercise 3: Modeling with h2o Exercise 4: Grid and random search with h2o Exercise 5: Grid search with h2o Exercise 6: Random search with h2o Exercise 7: Stopping criteria Exercise 8: Automatic machine learning with H2O Exercise 9: AutoML in h2o Exercise 10: Scoring the leaderboard Exercise 11: Extract h2o models and evaluate performance Exercise 12: Wrap-up