[DOCS] Edits ML hyperparameter descriptions (#68880) (#68934)

Author: lcawl

docs/reference/ml/ml-shared.asciidoc

@@ -537,24 +537,24 @@ the detectors in the `analysis_config`, starting at zero.
 end::detector-index[]
 
 tag::dfas-alpha[]
-Advanced configuration option. {ml-cap} uses loss guided tree growing.
-This means that trees will grow where the regularized loss reduces
-the most. This parameter multiplies a term based on tree depth in
-the regularized loss. Higher values result in shallower trees
-and faster training times. Values should be greater than or equal
-to zero. By default, this value is calculated during hyperparameter optimization.
+Advanced configuration option. {ml-cap} uses loss guided tree growing, which
+means that the decision trees grow where the regularized loss decreases most
+quickly. This parameter affects loss calculations by acting as a multiplier of 
+the tree depth. Higher alpha values result in shallower trees and faster 
+training times. By default, this value is calculated during hyperparameter 
+optimization. It must be greater than or equal to zero. 
 end::dfas-alpha[]
 
 tag::dfas-downsample-factor[]
-Advanced configuration option. This controls the fraction of data
-that is used to compute the derivatives of the loss function for tree training.
-The lower the value the smaller the fraction of data that is used.
-Typically accuracy improves if this is set to be less than 1. However, too small
-a value may result in poor convergence for the ensemble and so require more trees.
-For more information about shrinkage, refer to
+Advanced configuration option. Controls the fraction of data that is used to 
+compute the derivatives of the loss function for tree training. A small value 
+results in the use of a small fraction of the data. If this value is set to be 
+less than 1, accuracy typically improves. However, too small a value may result 
+in poor convergence for the ensemble and so require more trees. For more
+information about shrinkage, refer to
 {wikipedia}/Gradient_boosting#Stochastic_gradient_boosting[this wiki article].
-Values must be greater than zero and less than or equal to 1.
-By default, this value is calculated during hyperparameter optimization.
+By default, this value is calculated during hyperparameter optimization. It 
+must be greater than zero and less than or equal to 1.
 end::dfas-downsample-factor[]
 
 tag::dfas-early-stopping-enabled[]
@@ -566,11 +566,10 @@ By default, early stoppping is enabled.
 end::dfas-early-stopping-enabled[]
 
 tag::dfas-eta-growth[]
-Advanced configuration option.
-Specifies the rate at which `eta` increases for each new tree that is added
-to the forest. For example, a rate of `1.05` increases `eta` by 5% for each
-extra tree. Values must be in the range of 0.5 to 2.
-By default, this value is calculated during hyperparameter optimization.
+Advanced configuration option. Specifies the rate at which `eta` increases for 
+each new tree that is added to the forest. For example, a rate of 1.05 
+increases `eta` by 5% for each extra tree. By default, this value is calculated 
+during hyperparameter optimization. It must be between 0.5 and 2.
 end::dfas-eta-growth[]
 
 tag::dfas-feature-processors[]
@@ -696,18 +695,18 @@ end::dfas-num-splits[]
 
 tag::dfas-soft-limit[]
 Advanced configuration option. {ml-cap} uses loss guided tree growing, which 
-means that the decision trees grow where the regularized loss decreases most quickly. This
-soft limit combines with the `soft_tree_depth_tolerance` to penalize trees that 
-exceed the specified depth; the regularized loss increases quickly beyond this 
-depth. Values must be greater than or equal to 0. By default, this value is 
-calculated during hyperparameter optimization.
+means that the decision trees grow where the regularized loss decreases most 
+quickly. This soft limit combines with the `soft_tree_depth_tolerance` to 
+penalize trees that exceed the specified depth; the regularized loss increases 
+quickly beyond this depth. By default, this value is calculated during 
+hyperparameter optimization. It must be greater than or equal to 0.
 end::dfas-soft-limit[]
 
 tag::dfas-soft-tolerance[]
 Advanced configuration option. This option controls how quickly the regularized 
-loss increases when the tree depth exceeds `soft_tree_depth_limit`. Values must 
-be greater than or equal to 0.01. By default, this value is calculated during 
-hyperparameter optimization.
+loss increases when the tree depth exceeds `soft_tree_depth_limit`. By default, 
+this value is calculated during hyperparameter optimization. It must be greater 
+than or equal to 0.01. 
 end::dfas-soft-tolerance[]
 
 tag::dfas-timestamp[]
@@ -753,10 +752,11 @@ end::empty-bucket-count[]
 tag::eta[]
 Advanced configuration option. The shrinkage applied to the weights. Smaller
 values result in larger forests which have a better generalization error.
-However, the smaller the value the longer the training will take. For more
-information about shrinkage, refer to
+However, larger forests cause slower training. For more information about 
+shrinkage, refer to
 {wikipedia}/Gradient_boosting#Shrinkage[this wiki article].
-By default, this value is calculated during hyperparameter optimization.
+By default, this value is calculated during hyperparameter optimization. It must
+be a value between 0.001 and 1.
 end::eta[]
 
 tag::exclude-frequent[]
@@ -842,11 +842,11 @@ end::function[]
 
 tag::gamma[]
 Advanced configuration option. Regularization parameter to prevent overfitting
-on the training data set. Multiplies a linear penalty associated with the size of
-individual trees in the forest. The higher the value the more training will
-prefer smaller trees. The smaller this parameter the larger individual trees
-will be and the longer training will take. By default, this value is calculated
-during hyperparameter optimization.
+on the training data set. Multiplies a linear penalty associated with the size 
+of individual trees in the forest. A high gamma value causes training to prefer 
+small trees. A small gamma value results in larger individual trees and slower 
+training. By default, this value is calculated during hyperparameter 
+optimization. It must be a nonnegative value.
 end::gamma[]
 
 tag::groups[]
@@ -1046,13 +1046,14 @@ end::jobs-stats-anomaly-detection[]
 
 tag::lambda[]
 Advanced configuration option. Regularization parameter to prevent overfitting
-on the training data set. Multiplies an L2 regularisation term which applies to
-leaf weights of the individual trees in the forest. The higher the value the
-more training will attempt to keep leaf weights small. This makes the prediction
+on the training data set. Multiplies an L2 regularization term which applies to
+leaf weights of the individual trees in the forest. A high lambda value causes 
+training to favor small leaf weights. This behavior makes the prediction 
 function smoother at the expense of potentially not being able to capture
-relevant relationships between the features and the {depvar}. The smaller this
-parameter the larger individual trees will be and the longer training will take.
-By default, this value is calculated during hyperparameter optimization.
+relevant relationships between the features and the {depvar}. A small lambda
+value results in large individual trees and slower training. By default, this 
+value is calculated during hyperparameter optimization. It must be a nonnegative
+value.
 end::lambda[]
 
 tag::last-data-time[]
@@ -1095,9 +1096,9 @@ set.
 end::max-empty-searches[]
 
 tag::max-trees[]
-Advanced configuration option. Defines the maximum number of trees the forest is
-allowed to contain. The maximum value is 2000. By default, this value is
-calculated during hyperparameter optimization.
+Advanced configuration option. Defines the maximum number of decision trees in 
+the forest. The maximum value is 2000. By default, this value is calculated 
+during hyperparameter optimization.
 end::max-trees[]
 
 tag::method[]
@@ -1386,11 +1387,10 @@ multiple jobs running on the same node. For more information, see
 end::query-delay[]
 
 tag::randomize-seed[]
-Defines the seed to the random generator that is used to pick
-which documents will be used for training. By default it is randomly generated.
-Set it to a specific value to ensure the same documents are used for training
-assuming other related parameters (e.g. `source`, `analyzed_fields`, etc.) are
-the same.
+Defines the seed for the random generator that is used to pick training data. By 
+default, it is randomly generated. Set it to a specific value to use the same 
+training data each time you start a job (assuming other related parameters such 
+as `source` and `analyzed_fields` are the same).
 end::randomize-seed[]
 
 tag::rare-category-count[]