caret ile Uyarlamalı Yeniden Örnekleme

Şimdi Uyarlamalı Yeniden Örnekleme kullanarak seçmen verisi üzerinde bir model eğiteceksin!

Daha önce olduğu gibi, küçük voters_train_data veri kümesi senin için yüklendi; ayrıca caret ve tictoc paketleri de hazır.

Bu egzersiz

R'de Hiperparametre Ayarlama

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Define trainControl function
fitControl <- trainControl(___,
                           number = ___, repeats = ___)

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: caret ile hiperparametre ayarı Exercise 2: Hiperparametreleri bulma Exercise 3: caret ile Kartezyen ızgara araması Exercise 4: Hiperparametre model çıktısını görselleştir Exercise 5: Izgara ve Rastgele Arama Exercise 6: Hiperparametre aralıklarıyla ızgara araması Exercise 7: Rastgele arama için train() seçeneğini bul Exercise 8: caret ile rastgele arama Exercise 9: Uyarlanabilir yeniden örnekleme Exercise 10: Adaptif Yeniden Örneklemenin Avantajları Exercise 11: caret ile Uyarlamalı Yeniden Örnekleme

Geçerli Egzersiz

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