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Help on class H2OGridSearch in module h2o.grid.grid_search:

class H2OGridSearch(H2OGridSearch)
 |  Grid Search of a Hyper-Parameter Space for a Model
 |  
 |  :param model: The type of model to be explored initialized with optional parameters that will be
 |      unchanged across explored models.
 |  :param hyper_params: A dictionary of string parameters (keys) and a list of values to be explored by grid
 |      search (values).
 |  :param str grid_id: The unique id assigned to the resulting grid object. If none is given, an id will
 |      automatically be generated.
 |  :param search_criteria:  The optional dictionary of directives which control the search of the hyperparameter space.
 |      The dictionary can include values for: ``strategy``, ``max_models``, ``max_runtime_secs``, ``stopping_metric``, 
 |      ``stopping_tolerance``, ``stopping_rounds`` and ``seed``. The default strategy, "Cartesian", covers the entire space of 
 |      hyperparameter combinations. If you want to use cartesian grid search, you can leave the search_criteria 
 |      argument unspecified. Specify the "RandomDiscrete" strategy to get random search of all the combinations of 
 |      your hyperparameters with three ways of specifying when to stop the search: max number of models, max time, and 
 |      metric-based early stopping (e.g., stop if MSE hasn’t improved by 0.0001 over the 5 best models). 
 |      Examples below::
 |  
 |          >>> criteria = {"strategy": "RandomDiscrete", "max_runtime_secs": 600,
 |          ...             "max_models": 100, "stopping_metric": "AUTO",
 |          ...             "stopping_tolerance": 0.00001, "stopping_rounds": 5,
 |          ...             "seed": 123456}
 |          >>> criteria = {"strategy": "RandomDiscrete", "max_models": 42,
 |          ...             "max_runtime_secs": 28800, "seed": 1234}
 |          >>> criteria = {"strategy": "RandomDiscrete", "stopping_metric": "AUTO",
 |          ...             "stopping_tolerance": 0.001, "stopping_rounds": 10}
 |          >>> criteria = {"strategy": "RandomDiscrete", "stopping_rounds": 5,
 |          ...             "stopping_metric": "misclassification",
 |          ...             "stopping_tolerance": 0.00001}
 |  :param parallelism: Level of parallelism during grid model building. 1 = sequential building (default). 
 |       Use the value of 0 for adaptive parallelism - decided by H2O. Any number > 1 sets the exact number of models
 |       built in parallel.
 |  :returns: a new H2OGridSearch instance
 |  
 |  Examples
 |  --------
 |      >>> from h2o.grid.grid_search import H2OGridSearch
 |      >>> from h2o.estimators.glm import H2OGeneralizedLinearEstimator
 |      >>> hyper_parameters = {'alpha': [0.01,0.5], 'lambda': [1e-5,1e-6]}
 |      >>> gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='binomial'), hyper_parameters)
 |      >>> training_data = h2o.import_file("smalldata/logreg/benign.csv")
 |      >>> gs.train(x=range(3) + range(4,11),y=3, training_frame=training_data)
 |      >>> gs.show()
 |  
 |  Method resolution order:
 |      H2OGridSearch
 |      H2OGridSearch
 |      builtins.object
 |  
 |  Methods defined here:
 |  
 |  __getattr__(self, name)
 |  
 |  __getitem__(self, item)
 |  
 |  __init__(self, *args, **kwargs)
 |  
 |  __iter__(self)
 |  
 |  __len__(self)
 |  
 |  __repr__(self)
 |      Return repr(self).
 |  
 |  aic(self, train=False, valid=False, xval=False)
 |      Get the AIC(s).
 |      
 |      If all are False (default), then return the training metric value.
 |      If more than one options is set to True, then return a dictionary of metrics where the keys are "train",
 |      "valid", and "xval".
 |      
 |      :param bool train: If train is True, then return the AIC value for the training data.
 |      :param bool valid: If valid is True, then return the AIC value for the validation data.
 |      :param bool xval:  If xval is True, then return the AIC value for the validation data.
 |      
 |      :returns: The AIC.
 |  
 |  auc(self, train=False, valid=False, xval=False)
 |      Get the AUC(s).
 |      
 |      If all are False (default), then return the training metric value.
 |      If more than one options is set to True, then return a dictionary of metrics where the keys are "train",
 |      "valid", and "xval".
 |      
 |      :param bool train: If train is True, then return the AUC value for the training data.
 |      :param bool valid: If valid is True, then return the AUC value for the validation data.
 |      :param bool xval:  If xval is True, then return the AUC value for the validation data.
 |      
 |      :returns: The AUC.
 |  
 |  aucpr(self, train=False, valid=False, xval=False)
 |      Get the aucPR (Area Under PRECISION RECALL Curve).
 |      
 |      If all are False (default), then return the training metric value.
 |      If more than one options is set to True, then return a dictionary of metrics where the keys are "train",
 |      "valid", and "xval".
 |      
 |      :param bool train: If train is True, then return the aucpr value for the training data.
 |      :param bool valid: If valid is True, then return the aucpr value for the validation data.
 |      :param bool xval:  If xval is True, then return the aucpr value for the validation data.
 |      
 |      :returns: The AUCPR for the models in this grid.
 |  
 |  biases(self, vector_id=0)
 |      Return the frame for the respective bias vector.
 |      
 |      :param: vector_id: an integer, ranging from 0 to number of layers, that specifies the bias vector to return.
 |      :returns: an H2OFrame which represents the bias vector identified by vector_id
 |  
 |  build_model(self, algo_params)
 |      (internal)
 |  
 |  catoffsets(self)
 |      Categorical offsets for one-hot encoding
 |  
 |  coef(self)
 |      Return the coefficients that can be applied to the non-standardized data.
 |      
 |      Note: standardize = True by default. If set to False, then coef() returns the coefficients that are fit directly.
 |  
 |  coef_norm(self)
 |      Return coefficients fitted on the standardized data (requires standardize = True, which is on by default). These coefficients can be used to evaluate variable importance.
 |  
 |  deepfeatures(self, test_data, layer)
 |      Obtain a hidden layer's details on a dataset.
 |      
 |      :param test_data: Data to create a feature space on.
 |      :param int layer: Index of the hidden layer.
 |      :returns: A dictionary of hidden layer details for each model.
 |  
 |  get_grid(self, sort_by=None, decreasing=None)
 |      Retrieve an H2OGridSearch instance.
 |      
 |      Optionally specify a metric by which to sort models and a sort order.
 |      Note that if neither cross-validation nor a validation frame is used in the grid search, then the
 |      training metrics will display in the "get grid" output. If a validation frame is passed to the grid, and
 |      ``nfolds = 0``, then the validation metrics will display. However, if ``nfolds`` > 1, then cross-validation
 |      metrics will display even if a validation frame is provided.
 |      
 |      :param str sort_by: A metric by which to sort the models in the grid space. Choices are: ``"logloss"``,
 |          ``"residual_deviance"``, ``"mse"``, ``"auc"``, ``"r2"``, ``"accuracy"``, ``"precision"``, ``"recall"``,
 |          ``"f1"``, etc.
 |      :param bool decreasing: Sort the models in decreasing order of metric if true, otherwise sort in increasing
 |          order (default).
 |      
 |      :returns: A new H2OGridSearch instance optionally sorted on the specified metric.
 |  
 |  get_hyperparams(self, id, display=True)
 |      Get the hyperparameters of a model explored by grid search.
 |      
 |      :param str id: The model id of the model with hyperparameters of interest.
 |      :param bool display: Flag to indicate whether to display the hyperparameter names.
 |      
 |      :returns: A list of the hyperparameters for the specified model.
 |  
 |  get_hyperparams_dict(self, id, display=True)
 |      Derived and returned the model parameters used to train the particular grid search model.
 |      
 |      :param str id: The model id of the model with hyperparameters of interest.
 |      :param bool display: Flag to indicate whether to display the hyperparameter names.
 |      
 |      :returns: A dict of model pararmeters derived from the hyper-parameters used to train this particular model.
 |  
 |  get_xval_models(self, key=None)
 |      Return a Model object.
 |      
 |      :param str key: If None, return all cross-validated models; otherwise return the model
 |          specified by the key.
 |      :returns: A model or a list of models.
 |  
 |  gini(self, train=False, valid=False, xval=False)
 |      Get the Gini Coefficient(s).
 |      
 |      If all are False (default), then return the training metric value.
 |      If more than one options is set to True, then return a dictionary of metrics where the keys are "train",
 |      "valid", and "xval".
 |      
 |      :param bool train: If train is True, then return the Gini Coefficient value for the training data.
 |      :param bool valid: If valid is True, then return the Gini Coefficient value for the validation data.
 |      :param bool xval:  If xval is True, then return the Gini Coefficient value for the cross validation data.
 |      
 |      :returns: The Gini Coefficient for the models in this grid.
 |  
 |  is_cross_validated(self)
 |      Return True if the model was cross-validated.
 |  
 |  join(self)
 |      Wait until grid finishes computing.
 |  
 |  logloss(self, train=False, valid=False, xval=False)
 |      Get the Log Loss(s).
 |      
 |      If all are False (default), then return the training metric value.
 |      If more than one options is set to True, then return a dictionary of metrics where the keys are "train",
 |      "valid", and "xval".
 |      
 |      :param bool train: If train is True, then return the Log Loss value for the training data.
 |      :param bool valid: If valid is True, then return the Log Loss value for the validation data.
 |      :param bool xval:  If xval is True, then return the Log Loss value for the cross validation data.
 |      
 |      :returns: The Log Loss for this binomial model.
 |  
 |  mae(self, train=False, valid=False, xval=False)
 |  
 |  mean_residual_deviance(self, train=False, valid=False, xval=False)
 |      Get the Mean Residual Deviances(s).
 |      
 |      If all are False (default), then return the training metric value.
 |      If more than one options is set to True, then return a dictionary of metrics where the keys are "train",
 |      "valid", and "xval".
 |      
 |      :param bool train: If train is True, then return the Mean Residual Deviance value for the training data.
 |      :param bool valid: If valid is True, then return the Mean Residual Deviance value for the validation data.
 |      :param bool xval:  If xval is True, then return the Mean Residual Deviance value for the cross validation data.
 |      :returns: The Mean Residual Deviance for this regression model.
 |  
 |  model_performance(self, test_data=None, train=False, valid=False, xval=False)
 |      Generate model metrics for this model on test_data.
 |      
 |      :param test_data: Data set for which model metrics shall be computed against. All three of train, valid
 |          and xval arguments are ignored if test_data is not None.
 |      :param train: Report the training metrics for the model.
 |      :param valid: Report the validation metrics for the model.
 |      :param xval: Report the validation metrics for the model.
 |      :return: An object of class H2OModelMetrics.
 |  
 |  mse(self, train=False, valid=False, xval=False)
 |      Get the MSE(s).
 |      
 |      If all are False (default), then return the training metric value.
 |      If more than one options is set to True, then return a dictionary of metrics where the keys are "train",
 |      "valid", and "xval".
 |      
 |      :param bool train: If train is True, then return the MSE value for the training data.
 |      :param bool valid: If valid is True, then return the MSE value for the validation data.
 |      :param bool xval:  If xval is True, then return the MSE value for the cross validation data.
 |      :returns: The MSE for this regression model.
 |  
 |  normmul(self)
 |      Normalization/Standardization multipliers for numeric predictors.
 |  
 |  normsub(self)
 |      Normalization/Standardization offsets for numeric predictors.
 |  
 |  null_degrees_of_freedom(self, train=False, valid=False, xval=False)
 |      Retreive the null degress of freedom if this model has the attribute, or None otherwise.
 |      
 |      :param bool train: Get the null dof for the training set. If both train and valid are False, then train is
 |          selected by default.
 |      :param bool valid: Get the null dof for the validation set. If both train and valid are True, then train is
 |          selected by default.
 |      :param bool xval: Get the null dof for the cross-validated models.
 |      
 |      :returns: the null dof, or None if it is not present.
 |  
 |  null_deviance(self, train=False, valid=False, xval=False)
 |      Retreive the null deviance if this model has the attribute, or None otherwise.
 |      
 |      :param bool train: Get the null deviance for the training set. If both train and valid are False, then
 |          train is selected by default.
 |      :param bool valid: Get the null deviance for the validation set. If both train and valid are True, then
 |          train is selected by default.
 |      :param bool xval: Get the null deviance for the cross-validated models.
 |      
 |      :returns: the null deviance, or None if it is not present.
 |  
 |  pprint_coef(self)
 |      Pretty print the coefficents table (includes normalized coefficients).
 |  
 |  pr_auc(self)
 |      H2OGridSearch.pr_auc is deprecated, please use ``H2OGridSearch.aucpr`` instead.
 |  
 |  predict(self, test_data)
 |      Predict on a dataset.
 |      
 |      :param H2OFrame test_data: Data to be predicted on.
 |      :returns: H2OFrame filled with predictions.
 |  
 |  r2(self, train=False, valid=False, xval=False)
 |      Return the R^2 for this regression model.
 |      
 |      The R^2 value is defined to be ``1 - MSE/var``, where ``var`` is computed as ``sigma^2``.
 |      
 |      If all are False (default), then return the training metric value.
 |      If more than one options is set to True, then return a dictionary of metrics where the keys are "train",
 |      "valid", and "xval".
 |      
 |      :param bool train: If train is True, then return the R^2 value for the training data.
 |      :param bool valid: If valid is True, then return the R^2 value for the validation data.
 |      :param bool xval:  If xval is True, then return the R^2 value for the cross validation data.
 |      
 |      :returns: The R^2 for this regression model.
 |  
 |  residual_degrees_of_freedom(self, train=False, valid=False, xval=False)
 |      Retreive the residual degress of freedom if this model has the attribute, or None otherwise.
 |      
 |      :param bool train: Get the residual dof for the training set. If both train and valid are False, then
 |          train is selected by default.
 |      :param bool valid: Get the residual dof for the validation set. If both train and valid are True, then
 |          train is selected by default.
 |      :param bool xval: Get the residual dof for the cross-validated models.
 |      
 |      :returns: the residual degrees of freedom, or None if they are not present.
 |  
 |  residual_deviance(self, train=False, valid=False, xval=False)
 |      Retreive the residual deviance if this model has the attribute, or None otherwise.
 |      
 |      :param bool train: Get the residual deviance for the training set. If both train and valid are False,
 |          then train is selected by default.
 |      :param bool valid: Get the residual deviance for the validation set. If both train and valid are True,
 |          then train is selected by default.
 |      :param bool xval: Get the residual deviance for the cross-validated models.
 |      
 |      :returns: the residual deviance, or None if it is not present.
 |  
 |  respmul(self)
 |      Normalization/Standardization multipliers for numeric response.
 |  
 |  respsub(self)
 |      Normalization/Standardization offsets for numeric response.
 |  
 |  rmse(self, train=False, valid=False, xval=False)
 |  
 |  rmsle(self, train=False, valid=False, xval=False)
 |  
 |  scoring_history(self)
 |      Retrieve model scoring history.
 |      
 |      :returns: Score history (H2OTwoDimTable)
 |  
 |  show(self)
 |      Print models sorted by metric.
 |  
 |  sort_by(self, metric, increasing=True)
 |      grid.sort_by() is deprecated; use grid.get_grid() instead
 |      
 |      Deprecated since 2016-12-12, use grid.get_grid() instead.
 |  
 |  sorted_metric_table(self)
 |      Retrieve summary table of an H2O Grid Search.
 |      
 |      :returns: The summary table as an H2OTwoDimTable or a Pandas DataFrame.
 |  
 |  start(self, x, y=None, training_frame=None, offset_column=None, fold_column=None, weights_column=None, validation_frame=None, **params)
 |      Asynchronous model build by specifying the predictor columns, response column, and any
 |      additional frame-specific values.
 |      
 |      To block for results, call :meth:`join`.
 |      
 |      :param x: A list of column names or indices indicating the predictor columns.
 |      :param y: An index or a column name indicating the response column.
 |      :param training_frame: The H2OFrame having the columns indicated by x and y (as well as any
 |          additional columns specified by fold, offset, and weights).
 |      :param offset_column: The name or index of the column in training_frame that holds the offsets.
 |      :param fold_column: The name or index of the column in training_frame that holds the per-row fold
 |          assignments.
 |      :param weights_column: The name or index of the column in training_frame that holds the per-row weights.
 |      :param validation_frame: H2OFrame with validation data to be scored on while training.
 |  
 |  summary(self, header=True)
 |      Print a detailed summary of the explored models.
 |  
 |  train(self, x=None, y=None, training_frame=None, offset_column=None, fold_column=None, weights_column=None, validation_frame=None, **params)
 |      Train the model synchronously (i.e. do not return until the model finishes training).
 |      
 |      To train asynchronously call :meth:`start`.
 |      
 |      :param x: A list of column names or indices indicating the predictor columns.
 |      :param y: An index or a column name indicating the response column.
 |      :param training_frame: The H2OFrame having the columns indicated by x and y (as well as any
 |          additional columns specified by fold, offset, and weights).
 |      :param offset_column: The name or index of the column in training_frame that holds the offsets.
 |      :param fold_column: The name or index of the column in training_frame that holds the per-row fold
 |          assignments.
 |      :param weights_column: The name or index of the column in training_frame that holds the per-row weights.
 |      :param validation_frame: H2OFrame with validation data to be scored on while training.
 |  
 |  varimp(self, use_pandas=False)
 |      Pretty print the variable importances, or return them in a list/pandas DataFrame.
 |      
 |      :param bool use_pandas: If True, then the variable importances will be returned as a pandas data frame.
 |      
 |      :returns: A dictionary of lists or Pandas DataFrame instances.
 |  
 |  weights(self, matrix_id=0)
 |      Return the frame for the respective weight matrix.
 |      
 |      :param: matrix_id: an integer, ranging from 0 to number of layers, that specifies the weight matrix to return.
 |      :returns: an H2OFrame which represents the weight matrix identified by matrix_id
 |  
 |  xval_keys(self)
 |      Model keys for the cross-validated model.
 |  
 |  xvals(self)
 |      Return the list of cross-validated models.
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      list of weak references to the object (if defined)
 |  
 |  failed_params
 |  
 |  failed_raw_params
 |  
 |  failure_details
 |  
 |  failure_stack_traces
 |  
 |  grid_id
 |      A key that identifies this grid search object in H2O.
 |  
 |  hyper_names
 |  
 |  model_ids

                       alpha                   lambda           model_ids  \
0                     [0.87]  [4.9999999999999996E-6]   glm_grid_model_61   
1                      [0.4]                 [1.8E-5]   glm_grid_model_46   
2                     [0.07]                 [3.7E-5]   glm_grid_model_48   
3                     [0.07]                 [5.6E-5]   glm_grid_model_72   
4                     [0.48]                 [2.9E-5]   glm_grid_model_96   
5                     [0.88]                 [3.1E-5]   glm_grid_model_37   
6                     [0.18]   [8.099999999999999E-5]   glm_grid_model_86   
7                     [0.15]  [1.3099999999999999E-4]   glm_grid_model_30   
8                      [0.1]                [1.59E-4]   glm_grid_model_38   
9                     [0.06]                [2.12E-4]   glm_grid_model_78   
10                    [0.53]   [9.499999999999999E-5]   glm_grid_model_28   
11     [0.41000000000000003]                [1.37E-4]   glm_grid_model_67   
12                    [0.65]                [1.07E-4]   glm_grid_model_34   
13                    [0.93]                 [7.7E-5]  glm_grid_model_100   
14                    [0.52]                [1.37E-4]   glm_grid_model_42   
15                    [0.31]  [2.2999999999999998E-4]   glm_grid_model_22   
16                    [0.56]                [1.64E-4]   glm_grid_model_82   
17     [0.47000000000000003]  [1.8899999999999999E-4]   glm_grid_model_51   
18                    [0.05]                [4.17E-4]   glm_grid_model_16   
19                    [0.18]                [3.47E-4]   glm_grid_model_17   
20                    [0.92]                [1.45E-4]   glm_grid_model_93   
21                    [0.11]                [4.88E-4]   glm_grid_model_73   
22                    [0.16]                [4.93E-4]   glm_grid_model_14   
23                    [0.75]  [1.8099999999999998E-4]   glm_grid_model_11   
24                    [0.09]                [6.68E-4]    glm_grid_model_1   
25                    [0.78]                [1.88E-4]    glm_grid_model_2   
26                    [0.07]                [7.32E-4]   glm_grid_model_79   
27                    [0.17]   [5.459999999999999E-4]   glm_grid_model_98   
28                    [0.65]                [2.26E-4]   glm_grid_model_83   
29                    [0.02]                [8.87E-4]   glm_grid_model_92   
.. ..                    ...                      ...                 ...   
70                    [0.55]   [6.129999999999999E-4]   glm_grid_model_59   
71                    [0.86]                [4.86E-4]   glm_grid_model_21   
72                    [0.63]                [5.75E-4]   glm_grid_model_49   
73                     [0.5]   [6.929999999999999E-4]    glm_grid_model_6   
74                    [0.73]                [5.53E-4]   glm_grid_model_13   
75                    [0.65]                [5.88E-4]   glm_grid_model_89   
76                    [0.87]   [5.099999999999999E-4]   glm_grid_model_19   
77                    [0.32]                 [9.5E-4]   glm_grid_model_95   
78                    [0.88]                [5.18E-4]   glm_grid_model_63   
79                    [0.42]   [8.129999999999999E-4]   glm_grid_model_74   
80                    [0.43]                [8.42E-4]   glm_grid_model_40   
81                    [0.66]                [6.59E-4]   glm_grid_model_18   
82                    [0.59]                [7.31E-4]    glm_grid_model_9   
83     [0.41000000000000003]                [8.91E-4]   glm_grid_model_24   
84                    [0.54]                 [7.8E-4]    glm_grid_model_5   
85     [0.41000000000000003]   [9.559999999999999E-4]   glm_grid_model_76   
86      [0.9400000000000001]                [5.09E-4]   glm_grid_model_62   
87     [0.41000000000000003]                [9.93E-4]   glm_grid_model_50   
88                     [0.6]                [8.16E-4]   glm_grid_model_32   
89                    [0.55]   [8.759999999999999E-4]   glm_grid_model_97   
90                    [0.52]                [9.45E-4]   glm_grid_model_91   
91                    [0.84]                [6.74E-4]   glm_grid_model_57   
92      [0.5700000000000001]                [9.53E-4]   glm_grid_model_69   
93                    [0.65]   [9.379999999999999E-4]   glm_grid_model_56   
94      [0.9500000000000001]                [7.41E-4]   glm_grid_model_85   
95                    [0.86]                [8.06E-4]   glm_grid_model_39   
96                    [0.92]                 [8.6E-4]   glm_grid_model_99   
97                    [0.74]                [9.87E-4]   glm_grid_model_15   
98                    [0.88]   [9.209999999999999E-4]   glm_grid_model_23   
99                    [0.88]                [9.48E-4]    glm_grid_model_7   

                   auc  
0   0.8460502420206815  
1   0.8459231218911992  
2   0.8458460074966513  
3        0.84578969349  
4    0.845718421725669  
5   0.8454975463710519  
6   0.8454049383408971  
7    0.845125597663872  
8    0.844990163622469  
9   0.8447922633836474  
10  0.8446763576704011  
11  0.8443068880489722  
12  0.8442450331686066  
13  0.8441362707601581  
14  0.8440820898598193  
15  0.8436148511314989  
16  0.8436089096500138  
17  0.8435708987360638  
18   0.843518778104261  
19  0.8432349813161218  
20  0.8426307867571391  
21  0.8426180947745795  
22  0.8421399824067635  
23  0.8416523933239516  
24  0.8416254797655253  
25  0.8415763740974008  
26   0.841510364862424  
27  0.8414721822594293  
28  0.8414650545627264  
29  0.8413966312757275  
..                 ...  
70  0.8367248032825947  
71   0.836457462629252  
72   0.836414769286811  
73  0.8362693772808174  
74  0.8362422374050139  
75  0.8362208751256985  
76  0.8361702996952978  
77   0.835997372408428  
78   0.835987266166935  
79  0.8359603526085087  
80  0.8355779204642072  
81  0.8355752566826656  
82  0.8353492462648034  
83  0.8353201996000644  
84  0.8352437797657427  
85  0.8348899104352362  
86   0.834719503855704  
87  0.8346606457295727  
88  0.8339922654813755  
89  0.8338713885898811  
90  0.8336982271815869  
91  0.8335408845775446  
92  0.8330923235362049  
93  0.8328250505179406  
94  0.8325835828833598  
95  0.8324377694588019  
96  0.8318016979671923  
97  0.8317961336813356  
98  0.8315479207483787  
99  0.8313888092269064  

[100 rows x 5 columns]

Model Details
=============
H2OGeneralizedLinearEstimator :  Generalized Linear Modeling
Model Key:  glm_grid_model_61


GLM Model: summary

ModelMetricsBinomialGLM: glm
** Reported on train data. **

MSE: 0.031344275605674536
RMSE: 0.17704314616972477
LogLoss: 0.12279979897845819
Null degrees of freedom: 350267
Residual degrees of freedom: 350124
Null deviance: 108932.13150368733
Residual deviance: 86025.67997717319
AIC: 86313.67997717319
AUC: 0.8519842670925402
AUCPR: 0.21046685420921254
Gini: 0.7039685341850803

Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.13993235618594693: 

Maximum Metrics: Maximum metrics at their respective thresholds

Gains/Lift Table: Avg response rate:  3.61 %, avg score:  3.61 %

ModelMetricsBinomialGLM: glm
** Reported on validation data. **

MSE: 0.031018805729749764
RMSE: 0.17612156520355413
LogLoss: 0.12242815235268398
Null degrees of freedom: 74970
Residual degrees of freedom: 74827
Null deviance: 22974.597464481732
Residual deviance: 18357.12202006614
AIC: 18645.12202006614
AUC: 0.8460502420206815
AUCPR: 0.2009137545141779
Gini: 0.6921004840413629

Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.13091994899463488: 

Maximum Metrics: Maximum metrics at their respective thresholds

Gains/Lift Table: Avg response rate:  3.55 %, avg score:  3.61 %

Scoring History: 

Grid Summary:

[[0.13993235618594693, 0.29243426359090174]]

ModelMetricsBinomialGLM: glm
** Reported on test data. **

MSE: 0.031143376101575086
RMSE: 0.17647485968708146
LogLoss: 0.12199693111453563
Null degrees of freedom: 74897
Residual degrees of freedom: 74754
Null deviance: 23061.156287645877
Residual deviance: 18274.652293232975
AIC: 18562.652293232975
AUC: 0.8524158062119054
AUCPR: 0.20258611034476104
Gini: 0.7048316124238108

Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.13069466877003805: 

Maximum Metrics: Maximum metrics at their respective thresholds

Gains/Lift Table: Avg response rate:  3.57 %, avg score:  3.60 %

drf Grid Build progress: |████████████████████████████████████████████████| 100%
CPU times: user 2.33 s, sys: 432 ms, total: 2.76 s
Wall time: 6min 45s

    max_depth ntrees          model_ids                auc
0          20     28  rf_grid_2_model_1  0.818864830103598

drf Model Build progress: |███████████████████████████████████████████████| 100%
CPU times: user 347 ms, sys: 109 ms, total: 456 ms
Wall time: 39.7 s

Model Summary: