3.11.0.3689
  • Welcome to H2O 3
  • Downloading H2O
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  • Starting H2O
  • EC2 Instances & S3 Storage
  • Using H2O on Hadoop
  • Using H2O with Docker
  • Data Manipulation
  • Using Flow - H2O’s Web UI
  • Data Science Algorithms
  • Cross-Validation
  • Grid (Hyperparameter) Search
  • Saving and Loading a Model
  • About POJOs and MOJOs
  • Downloading Logs
  • Productionizing H2O
  • H2O Architecture
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  • Migrating to H2O 3
  • Appendix A - Parameters
    • GBM Parameters
  • Appendix B - API Reference
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  • Appendix A - Parameters
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Appendix A - ParametersΒΆ

This Appendix provides detailed descriptions of parameters that can be specified in the H2O algorithms. Note that this Appendix is a work in progress.

  • GBM Parameters
    • balance_classes
    • build_tree_one_node
    • categorical_encoding
    • checkpoint
    • class_sampling_factors
    • col_sample_rate
    • col_sample_rate_change_per_level
    • col_sample_rate_per_tree
    • distribution
    • fold_assignment
    • fold_column
    • histogram_type
    • huber_alpha
    • ignored_columns
    • ignore_const_cols
    • keep_cross_validation_fold_assignment
    • keep_cross_validation_predictions
    • learn_rate
    • learn_rate_annealing
    • max_abs_leafnode_pred
    • max_after_balance_size
    • max_depth
    • max_hit_ratio_k
    • max_runtime_secs
    • min_rows
    • min_split_improvement
    • model_id
    • nbins
    • nbins_cats
    • nbins_top_level
    • nfolds
    • ntrees
    • offset_column
    • pred_noise_bandwidth
    • quantile_alpha
    • sample_rate
    • sample_rate_per_class
    • score_each_iteration
    • score_tree_interval
    • seed
    • stopping_metric
    • stopping_rounds
    • stopping_tolerance
    • training_frame
    • tweedie_power
    • validation_frame
    • weights_column
    • y
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© Copyright 2016 H2O, Inc. Last updated on Nov 15, 2016.

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