Hiperparametre ayarlama sonuçlarını değerlendirme

Burada, rpart paketiyle eğitilmiş bir karar ağacı için bir hiperparametre ayarlama çalışmasının sonuçlarını değerlendireceksin. knowledge_train_data veri kümesi ile mlr ve tidyverse paketleri senin için zaten yüklendi. Ayrıca aşağıdaki kod da çalıştırıldı:

task <- makeClassifTask(data = knowledge_train_data, 
                        target = "UNS")

lrn <- makeLearner(cl = "classif.rpart", fix.factors.prediction = TRUE)

param_set <- makeParamSet(
  makeIntegerParam("minsplit", lower = 1, upper = 30),
  makeIntegerParam("minbucket", lower = 1, upper = 30),
  makeIntegerParam("maxdepth", lower = 3, upper = 10)
)

ctrl_random <- makeTuneControlRandom(maxit = 10)

Bu egzersiz

R'de Hiperparametre Ayarlama

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Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Create holdout sampling
holdout <- makeResampleDesc(___)

# Perform tuning
lrn_tune <- tuneParams(learner = lrn, task = task, resampling = holdout, control = ctrl_random, par.set = param_set)

Kodu Düzenle ve Çalıştır

Bu egzersiz

R'de Hiperparametre Ayarlama

kursunun bir parçasıdır

AvançadoNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: Model parameters vs. hyperparameters Exercise 3: Hyperparameters in linear models Exercise 4: What are the coefficients?Exercise 5: Recap of machine learning basics Exercise 6: Machine learning with caret Exercise 7: Resampling schemes Exercise 8: Hyperparameter tuning in caret Exercise 9: Hyperparameters in Stochastic Gradient Boosting Exercise 10: Changing the number of hyperparameters to tune Exercise 11: Tune hyperparameters manually

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: mlr ile Machine Learning Exercise 2: mlr ile Machine Learning Exercise 3: mlr ile modelleme Exercise 4: mlr ile ızgara ve rastgele arama Exercise 5: mlr ile rastgele arama Exercise 6: mlr ile hiperparametre ayarı yap Exercise 7: mlr ile hiperparametreleri değerlendirme Exercise 8: Neden ayarlamayı değerlendirmelisin?Exercise 9: Hiperparametre ayarlama sonuçlarını değerlendirme

Geçerli Egzersiz

Exercise 10: mlr ile ileri düzey ayarlama Exercise 11: Gelişmiş ayar kontrolünü tanımla Exercise 12: Birleştirilmiş ölçütleri tanımla Exercise 13: Hiperparametreleri ayarlama

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