h2o ile rastgele arama

Şimdi rastgele arama kullanacaksın. h2o kütüphanesi ve seeds_train_data senin için zaten yüklendi ve aşağıdaki kod çalıştırıldı:

h2o.init()
seeds_train_data_hf <- as.h2o(seeds_train_data)

y <- "seed_type"
x <- setdiff(colnames(seeds_train_data_hf), y)

seeds_train_data_hf[, y] <- as.factor(seeds_train_data_hf[, y])

sframe <- h2o.splitFrame(seeds_train_data_hf, seed = 42)
train <- sframe[[1]]
valid <- sframe[[2]]

dl_params <- list(hidden = list(c(50, 50), c(100, 100)),
                  epochs = c(5, 10, 15),
                  rate = c(0.001, 0.005, 0.01))

Bu egzersiz

R'de Hiperparametre Ayarlama

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Rastgele aramayı tanımlayan ve maksimum çalışma süresi 10 saniye olan bir arama ölçütü nesnesi tanımla.
Bu arama ölçütü nesnesini h2o.grid fonksiyonunda uygun yere ekleyerek rastgele modelleri eğit.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Define search criteria
search_criteria <- list(strategy = ___, 
                        ___ = 10, # this is way too short & only used to keep runtime short!
                        seed = 42)

# Train with random search
dl_grid <- h2o.grid("deeplearning", 
                    grid_id = "dl_grid",
                    x = x, 
                    y = y,
                    training_frame = train,
                    validation_frame = valid,
                    seed = 42,
                    hyper_params = dl_params,
                    ___ = ___)

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: 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: h2o ile Machine Learning Exercise 2: h2o ile modelleme için veriyi hazırla Exercise 3: h2o ile modelleme Exercise 4: h2o ile grid ve rastgele arama Exercise 5: h2o ile grid search Exercise 6: h2o ile rastgele arama

Geçerli Egzersiz

Exercise 7: Durdurma kıstasları Exercise 8: H2O ile otomatik Machine Learning Exercise 9: h2o'da AutoML Exercise 10: Lider tablosunu puanlama Exercise 11: h2o modellerini çıkar ve performansı değerlendir Exercise 12: Kapanış