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- import h2o
- h2o.init(nthreads = -1, max_mem_size = "110g")
- df = h2o.import_file(path = "C:/user/path_to_data.csv")
- x = [col for col in df.column if 'target' not in col]
- y = 'target'
- #establish model
- m = h2o.estimators.H2ORandomForestEstimator(model_id="RF_defaults", nfolds = 5)
- m.train(x,y, df)
- #view results
- print (m.cross_validation_metrics_summary())
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