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`out_of_bounds`

  • Available in: Isotonic Regression
  • Hyperparameter: no

Description​

Use this option to specify how a trained model should treat values of an X predictor that are outside of the bounds seen in training.

Available options for out_of_bounds include the following:

  • na: Output NA for values that are outside of the interval seen during training. This is the default option.
  • clip: Use the prediction of the smallest or largest seen value depending on what side of the training interval the particular value falls in.

Example​

import h2o
from h2o import H2OFrame
from h2o.estimators.isotonicregression import H2OIsotonicRegressionEstimator
import numpy as np
from sklearn.datasets import make_regression
h2o.init()

X_full, y_full = make_regression(n_samples=10000, n_features=1, random_state=41, noise=0.8)
X_full = X_full.reshape(-1)

p05 = np.quantile(X_full, 0.05)
p95 = np.quantile(X_full, 0.95)

X = X_full[np.logical_and(p05 < X_full, X_full < p95)]
y = y_full[np.logical_and(p05 < X_full, X_full < p95)]

train = H2OFrame(np.column_stack((y, X)), column_names=["y", "X"])
h2o_iso_reg = H2OIsotonicRegressionEstimator(out_of_bounds="clip")
h2o_iso_reg.train(training_frame=train, x="X", y="y")

test = H2OFrame(np.column_stack((y_full, X_full)), column_names=["y", "X"])
h2o_test_preds = h2o_iso_reg.predict(test).as_data_frame()
print(h2o_test_preds)
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