Pencarian acak dengan h2o

Selanjutnya, Anda akan menggunakan random search. Pustaka h2o dan seeds_train_data telah dimuat untuk Anda dan kode berikut telah dijalankan:

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))

Latihan ini adalah bagian dari kursus

Penyetelan Hyperparameter di R

Petunjuk latihan

Definisikan sebuah objek search criteria yang menetapkan random search dengan waktu jalan maksimum 10 detik.
Tambahkan objek search criteria ini pada tempat yang sesuai dalam fungsi h2o.grid untuk melatih model acak.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# 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,
                    ___ = ___)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Penyetelan Hyperparameter di R

SkillTag.level.advancedSkillTag.label

4.8+

Mulai Kursus 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: 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: Machine Learning dengan h2o Exercise 2: Menyiapkan data untuk pemodelan dengan h2o Exercise 3: Pemodelan dengan h2o Exercise 4: Grid dan random search dengan h2o Exercise 5: Grid search dengan h2o Exercise 6: Pencarian acak dengan h2o

Latihan Saat Ini

Exercise 7: Kriteria penghentian Exercise 8: Machine learning otomatis dengan H2O Exercise 9: AutoML di h2o Exercise 10: Memberi skor pada leaderboard Exercise 11: Ekstrak model h2o dan evaluasi kinerja Exercise 12: Ringkasan