add py script

H2O_Example.py

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+# %%
+import h2o
+
+# %%
+h2o.__version__
+
+# %%
+h2o.init()
+
+# %%
+from h2o.estimators import H2OGradientBoostingEstimator
+h2o.init(jvm_custom_args=["sys.ai.h2o.debug.allowJavaVersions", "18"])
+
+# Import the prostate dataset into H2O:
+prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv")
+
+# Set the predictors and response; set the factors:
+prostate["CAPSULE"] = prostate["CAPSULE"].asfactor()
+predictors = ["ID","AGE","RACE","DPROS","DCAPS","PSA","VOL","GLEASON"]
+response = "CAPSULE"
+
+# Build and train the model:
+pros_gbm = H2OGradientBoostingEstimator(nfolds=5,
+                                        seed=1111,
+                                        keep_cross_validation_predictions = True)
+pros_gbm.train(x=predictors, y=response, training_frame=prostate)
+
+# Eval performance:
+perf = pros_gbm.model_performance()
+
+# Generate predictions on a test set (if necessary):
+pred = pros_gbm.predict(prostate)
+
+# Extract feature interactions:
+feature_interactions = pros_gbm.feature_interaction()
+
+# %%
+feature_interactions
+
+# %%
+#save model
+h2o.save_model(model=pros_gbm, force=True)
+
+# %%
+pros_gbm.save_mojo('mojo')
+
+