continue to add Veronika comments.

h2o-algos/src/main/java/hex/schemas/HGLMV3.java

@@ -65,7 +65,7 @@ public static final class HGLMParametersV3 extends ModelParametersSchemaV3<HGLMM
             values = {"gaussian"}, level = Level.critical)
     public GLMParameters.Family family;
 
-    @API(help = Set distribution of random effects.  Only Gaussian is implemented now.",
+    @API(help = "Set distribution of random effects.  Only Gaussian is implemented now.",
             values = {"gaussian"}, level = Level.critical)
     public GLMParameters.Family rand_family;
 

h2o-py/h2o/estimators/hglm.py

@@ -111,7 +111,7 @@ def __init__(self,
         :param family: Family. Only gaussian is supported now.
                Defaults to ``"gaussian"``.
         :type family: Literal["gaussian"]
-        :param rand_family: rand_family. Set distribution of random effects.  Only Gaussian is implemented now.
+        :param rand_family: Set distribution of random effects.  Only Gaussian is implemented now.
                Defaults to ``None``.
         :type rand_family: Literal["gaussian"], optional
         :param max_iterations: Maximum number of iterations.  Value should >=1.  A value of 0 is only set when only the
@@ -141,7 +141,7 @@ def __init__(self,
                will be randomly set in the model building process.
                Defaults to ``0.0``.
         :type tau_e_var_init: float
-        :param random_columns: random columns indices for HGLM.
+        :param random_columns: Random columns indices for HGLM.
                Defaults to ``None``.
         :type random_columns: List[str], optional
         :param method: We only implemented EM as a method to obtain the fixed, random coefficients and the various
@@ -152,10 +152,10 @@ def __init__(self,
                applies to EM method.
                Defaults to ``0.001``.
         :type em_epsilon: float
-        :param random_intercept: if true, will allow random component to the GLM coefficients.
+        :param random_intercept: If true, will allow random component to the GLM coefficients.
                Defaults to ``True``.
         :type random_intercept: bool
-        :param group_column: group_column is the column that is categorical and used to generate the groups in HGLM
+        :param group_column: Group column is the column that is categorical and used to generate the groups in HGLM
                Defaults to ``None``.
         :type group_column: str, optional
         :param gen_syn_data: If true, add gaussian noise with variance specified in parms._tau_e_var_init.
@@ -411,7 +411,7 @@ def family(self, family):
     @property
     def rand_family(self):
         """
-        rand_family. Set distribution of random effects.  Only Gaussian is implemented now.
+        Set distribution of random effects.  Only Gaussian is implemented now.
 
         Type: ``Literal["gaussian"]``.
         """
@@ -515,7 +515,7 @@ def tau_e_var_init(self, tau_e_var_init):
     @property
     def random_columns(self):
         """
-        random columns indices for HGLM.
+        Random columns indices for HGLM.
 
         Type: ``List[str]``.
 
@@ -569,7 +569,7 @@ def em_epsilon(self, em_epsilon):
     @property
     def random_intercept(self):
         """
-        if true, will allow random component to the GLM coefficients.
+        If true, will allow random component to the GLM coefficients.
 
         Type: ``bool``, defaults to ``True``.
         """
@@ -583,7 +583,7 @@ def random_intercept(self, random_intercept):
     @property
     def group_column(self):
         """
-        group_column is the column that is categorical and used to generate the groups in HGLM
+        Group column is the column that is categorical and used to generate the groups in HGLM
 
         Type: ``str``.
         """

h2o-py/h2o/estimators/model_selection.py

@@ -227,7 +227,7 @@ def __init__(self,
                lambda_search is set to True, the conditional values above are 1E-8 and 1E-6 respectively.
                Defaults to ``-1.0``.
         :type gradient_epsilon: float
-        :param startval: double array to initialize coefficients for GLM.
+        :param startval: Double array to initialize coefficients for GLM.
                Defaults to ``None``.
         :type startval: List[float], optional
         :param prior: Prior probability for y==1. To be used only for logistic regression iff the data has been sampled
@@ -900,7 +900,7 @@ def gradient_epsilon(self, gradient_epsilon):
     @property
     def startval(self):
         """
-        double array to initialize coefficients for GLM.
+        Double array to initialize coefficients for GLM.
 
         Type: ``List[float]``.
         """

h2o-r/h2o-package/R/hglm.R

@@ -14,8 +14,8 @@
 #'        The response must be either a numeric or a categorical/factor variable. 
 #'        If the response is numeric, then a regression model will be trained, otherwise it will train a classification model.
 #' @param training_frame Id of the training data frame.
-#' @param random_columns random columns indices for HGLM.
-#' @param group_column group_column is the column that is categorical and used to generate the groups in HGLM
+#' @param random_columns Random columns indices for HGLM.
+#' @param group_column Group column is the column that is categorical and used to generate the groups in HGLM
 #' @param model_id Destination id for this model; auto-generated if not specified.
 #' @param validation_frame Id of the validation data frame.
 #' @param ignore_const_cols \code{Logical}. Ignore constant columns. Defaults to TRUE.
@@ -38,8 +38,7 @@
 #' @param plug_values Plug Values (a single row frame containing values that will be used to impute missing values of the
 #'        training/validation frame, use with conjunction missing_values_handling = PlugValues).
 #' @param family Family. Only gaussian is supported now. Must be one of: "gaussian". Defaults to gaussian.
-#' @param rand_family rand_family. Set distribution of random effects.  Only Gaussian is implemented now. Must be one of:
-#'        "gaussian".
+#' @param rand_family Set distribution of random effects.  Only Gaussian is implemented now. Must be one of: "gaussian".
 #' @param max_iterations Maximum number of iterations.  Value should >=1.  A value of 0 is only set when only the model coefficient
 #'        names and model coefficient dimensions are needed. Defaults to -1.
 #' @param initial_fixed_effects An array that contains initial values of the fixed effects coefficient.
@@ -58,7 +57,7 @@
 #'        one of: "EM". Defaults to EM.
 #' @param em_epsilon Converge if beta/ubeta/tmat/tauEVar changes less (using L-infinity norm) than em esilon. ONLY applies to EM
 #'        method. Defaults to 0.001.
-#' @param random_intercept \code{Logical}. if true, will allow random component to the GLM coefficients. Defaults to TRUE.
+#' @param random_intercept \code{Logical}. If true, will allow random component to the GLM coefficients. Defaults to TRUE.
 #' @param gen_syn_data \code{Logical}. If true, add gaussian noise with variance specified in parms._tau_e_var_init. Defaults to
 #'        FALSE.
 #' @examples

h2o-r/h2o-package/R/modelselection.R

@@ -77,7 +77,7 @@
 #'        (of -1.0) indicates: If lambda_search is set to False and lambda is equal to zero, the default value of
 #'        gradient_epsilon is equal to .000001, otherwise the default value is .0001. If lambda_search is set to True,
 #'        the conditional values above are 1E-8 and 1E-6 respectively. Defaults to -1.
-#' @param startval double array to initialize coefficients for GLM.
+#' @param startval Double array to initialize coefficients for GLM.
 #' @param prior Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean
 #'        of response does not reflect reality. Defaults to 0.
 #' @param cold_start \code{Logical}. Only applicable to multiple alpha/lambda values.  If false, build the next model for next set