DOC use "fraction" and not "percentage" (scikit-learn#10653)

Percentage would imply that the value would be divided by 100.

Author: luispedro

sklearn/ensemble/forest.py

@@ -767,7 +767,7 @@ class RandomForestClassifier(ForestClassifier):
         The number of features to consider when looking for the best split:
 
         - If int, then consider `max_features` features at each split.
-        - If float, then `max_features` is a percentage and
+        - If float, then `max_features` is a fraction and
           `int(max_features * n_features)` features are considered at each
           split.
         - If "auto", then `max_features=sqrt(n_features)`.
@@ -788,23 +788,23 @@ class RandomForestClassifier(ForestClassifier):
         The minimum number of samples required to split an internal node:
 
         - If int, then consider `min_samples_split` as the minimum number.
-        - If float, then `min_samples_split` is a percentage and
+        - If float, then `min_samples_split` is a fraction and
           `ceil(min_samples_split * n_samples)` are the minimum
           number of samples for each split.
 
         .. versionchanged:: 0.18
-           Added float values for percentages.
+           Added float values for fractions.
 
     min_samples_leaf : int, float, optional (default=1)
         The minimum number of samples required to be at a leaf node:
 
         - If int, then consider `min_samples_leaf` as the minimum number.
-        - If float, then `min_samples_leaf` is a percentage and
+        - If float, then `min_samples_leaf` is a fraction and
           `ceil(min_samples_leaf * n_samples)` are the minimum
           number of samples for each node.
 
         .. versionchanged:: 0.18
-           Added float values for percentages.
+           Added float values for fractions.
 
     min_weight_fraction_leaf : float, optional (default=0.)
         The minimum weighted fraction of the sum total of weights (of all
@@ -1045,7 +1045,7 @@ class RandomForestRegressor(ForestRegressor):
         The number of features to consider when looking for the best split:
 
         - If int, then consider `max_features` features at each split.
-        - If float, then `max_features` is a percentage and
+        - If float, then `max_features` is a fraction and
           `int(max_features * n_features)` features are considered at each
           split.
         - If "auto", then `max_features=n_features`.
@@ -1066,23 +1066,23 @@ class RandomForestRegressor(ForestRegressor):
         The minimum number of samples required to split an internal node:
 
         - If int, then consider `min_samples_split` as the minimum number.
-        - If float, then `min_samples_split` is a percentage and
+        - If float, then `min_samples_split` is a fraction and
           `ceil(min_samples_split * n_samples)` are the minimum
           number of samples for each split.
 
         .. versionchanged:: 0.18
-           Added float values for percentages.
+           Added float values for fractions.
 
     min_samples_leaf : int, float, optional (default=1)
         The minimum number of samples required to be at a leaf node:
 
         - If int, then consider `min_samples_leaf` as the minimum number.
-        - If float, then `min_samples_leaf` is a percentage and
+        - If float, then `min_samples_leaf` is a fraction and
           `ceil(min_samples_leaf * n_samples)` are the minimum
           number of samples for each node.
 
         .. versionchanged:: 0.18
-           Added float values for percentages.
+           Added float values for fractions.
 
     min_weight_fraction_leaf : float, optional (default=0.)
         The minimum weighted fraction of the sum total of weights (of all
@@ -1276,7 +1276,7 @@ class ExtraTreesClassifier(ForestClassifier):
         The number of features to consider when looking for the best split:
 
         - If int, then consider `max_features` features at each split.
-        - If float, then `max_features` is a percentage and
+        - If float, then `max_features` is a fraction and
           `int(max_features * n_features)` features are considered at each
           split.
         - If "auto", then `max_features=sqrt(n_features)`.
@@ -1297,23 +1297,23 @@ class ExtraTreesClassifier(ForestClassifier):
         The minimum number of samples required to split an internal node:
 
         - If int, then consider `min_samples_split` as the minimum number.
-        - If float, then `min_samples_split` is a percentage and
+        - If float, then `min_samples_split` is a fraction and
           `ceil(min_samples_split * n_samples)` are the minimum
           number of samples for each split.
 
         .. versionchanged:: 0.18
-           Added float values for percentages.
+           Added float values for fractions.
 
     min_samples_leaf : int, float, optional (default=1)
         The minimum number of samples required to be at a leaf node:
 
         - If int, then consider `min_samples_leaf` as the minimum number.
-        - If float, then `min_samples_leaf` is a percentage and
+        - If float, then `min_samples_leaf` is a fraction and
           `ceil(min_samples_leaf * n_samples)` are the minimum
           number of samples for each node.
 
         .. versionchanged:: 0.18
-           Added float values for percentages.
+           Added float values for fractions.
 
     min_weight_fraction_leaf : float, optional (default=0.)
         The minimum weighted fraction of the sum total of weights (of all
@@ -1525,7 +1525,7 @@ class ExtraTreesRegressor(ForestRegressor):
         The number of features to consider when looking for the best split:
 
         - If int, then consider `max_features` features at each split.
-        - If float, then `max_features` is a percentage and
+        - If float, then `max_features` is a fraction and
           `int(max_features * n_features)` features are considered at each
           split.
         - If "auto", then `max_features=n_features`.
@@ -1546,23 +1546,23 @@ class ExtraTreesRegressor(ForestRegressor):
         The minimum number of samples required to split an internal node:
 
         - If int, then consider `min_samples_split` as the minimum number.
-        - If float, then `min_samples_split` is a percentage and
+        - If float, then `min_samples_split` is a fraction and
           `ceil(min_samples_split * n_samples)` are the minimum
           number of samples for each split.
 
         .. versionchanged:: 0.18
-           Added float values for percentages.
+           Added float values for fractions.
 
     min_samples_leaf : int, float, optional (default=1)
         The minimum number of samples required to be at a leaf node:
 
         - If int, then consider `min_samples_leaf` as the minimum number.
-        - If float, then `min_samples_leaf` is a percentage and
+        - If float, then `min_samples_leaf` is a fraction and
           `ceil(min_samples_leaf * n_samples)` are the minimum
           number of samples for each node.
 
         .. versionchanged:: 0.18
-           Added float values for percentages.
+           Added float values for fractions.
 
     min_weight_fraction_leaf : float, optional (default=0.)
         The minimum weighted fraction of the sum total of weights (of all
@@ -1736,23 +1736,23 @@ class RandomTreesEmbedding(BaseForest):
         The minimum number of samples required to split an internal node:
 
         - If int, then consider `min_samples_split` as the minimum number.
-        - If float, then `min_samples_split` is a percentage and
+        - If float, then `min_samples_split` is a fraction and
           `ceil(min_samples_split * n_samples)` is the minimum
           number of samples for each split.
 
         .. versionchanged:: 0.18
-           Added float values for percentages.
+           Added float values for fractions.
 
     min_samples_leaf : int, float, optional (default=1)
         The minimum number of samples required to be at a leaf node:
 
         - If int, then consider `min_samples_leaf` as the minimum number.
-        - If float, then `min_samples_leaf` is a percentage and
+        - If float, then `min_samples_leaf` is a fraction and
           `ceil(min_samples_leaf * n_samples)` is the minimum
           number of samples for each node.
 
         .. versionchanged:: 0.18
-           Added float values for percentages.
+           Added float values for fractions.
 
     min_weight_fraction_leaf : float, optional (default=0.)
         The minimum weighted fraction of the sum total of weights (of all

sklearn/ensemble/gradient_boosting.py

@@ -1330,23 +1330,23 @@ class GradientBoostingClassifier(BaseGradientBoosting, ClassifierMixin):
         The minimum number of samples required to split an internal node:
 
         - If int, then consider `min_samples_split` as the minimum number.
-        - If float, then `min_samples_split` is a percentage and
+        - If float, then `min_samples_split` is a fraction and
           `ceil(min_samples_split * n_samples)` are the minimum
           number of samples for each split.
 
         .. versionchanged:: 0.18
-           Added float values for percentages.
+           Added float values for fractions.
 
     min_samples_leaf : int, float, optional (default=1)
         The minimum number of samples required to be at a leaf node:
 
         - If int, then consider `min_samples_leaf` as the minimum number.
-        - If float, then `min_samples_leaf` is a percentage and
+        - If float, then `min_samples_leaf` is a fraction and
           `ceil(min_samples_leaf * n_samples)` are the minimum
           number of samples for each node.
 
         .. versionchanged:: 0.18
-           Added float values for percentages.
+           Added float values for fractions.
 
     min_weight_fraction_leaf : float, optional (default=0.)
         The minimum weighted fraction of the sum total of weights (of all
@@ -1364,7 +1364,7 @@ class GradientBoostingClassifier(BaseGradientBoosting, ClassifierMixin):
         The number of features to consider when looking for the best split:
 
         - If int, then consider `max_features` features at each split.
-        - If float, then `max_features` is a percentage and
+        - If float, then `max_features` is a fraction and
           `int(max_features * n_features)` features are considered at each
           split.
         - If "auto", then `max_features=sqrt(n_features)`.
@@ -1784,23 +1784,23 @@ class GradientBoostingRegressor(BaseGradientBoosting, RegressorMixin):
         The minimum number of samples required to split an internal node:
 
         - If int, then consider `min_samples_split` as the minimum number.
-        - If float, then `min_samples_split` is a percentage and
+        - If float, then `min_samples_split` is a fraction and
           `ceil(min_samples_split * n_samples)` are the minimum
           number of samples for each split.
 
         .. versionchanged:: 0.18
-           Added float values for percentages.
+           Added float values for fractions.
 
     min_samples_leaf : int, float, optional (default=1)
         The minimum number of samples required to be at a leaf node:
 
         - If int, then consider `min_samples_leaf` as the minimum number.
-        - If float, then `min_samples_leaf` is a percentage and
+        - If float, then `min_samples_leaf` is a fraction and
           `ceil(min_samples_leaf * n_samples)` are the minimum
           number of samples for each node.
 
         .. versionchanged:: 0.18
-           Added float values for percentages.
+           Added float values for fractions.
 
     min_weight_fraction_leaf : float, optional (default=0.)
         The minimum weighted fraction of the sum total of weights (of all
@@ -1818,7 +1818,7 @@ class GradientBoostingRegressor(BaseGradientBoosting, RegressorMixin):
         The number of features to consider when looking for the best split:
 
         - If int, then consider `max_features` features at each split.
-        - If float, then `max_features` is a percentage and
+        - If float, then `max_features` is a fraction and
           `int(max_features * n_features)` features are considered at each
           split.
         - If "auto", then `max_features=n_features`.

sklearn/tree/tree.py

@@ -540,23 +540,23 @@ class DecisionTreeClassifier(BaseDecisionTree, ClassifierMixin):
         The minimum number of samples required to split an internal node:
 
         - If int, then consider `min_samples_split` as the minimum number.
-        - If float, then `min_samples_split` is a percentage and
+        - If float, then `min_samples_split` is a fraction and
           `ceil(min_samples_split * n_samples)` are the minimum
           number of samples for each split.
 
         .. versionchanged:: 0.18
-           Added float values for percentages.
+           Added float values for fractions.
 
     min_samples_leaf : int, float, optional (default=1)
         The minimum number of samples required to be at a leaf node:
 
         - If int, then consider `min_samples_leaf` as the minimum number.
-        - If float, then `min_samples_leaf` is a percentage and
+        - If float, then `min_samples_leaf` is a fraction and
           `ceil(min_samples_leaf * n_samples)` are the minimum
           number of samples for each node.
 
         .. versionchanged:: 0.18
-           Added float values for percentages.
+           Added float values for fractions.
 
     min_weight_fraction_leaf : float, optional (default=0.)
         The minimum weighted fraction of the sum total of weights (of all
@@ -567,7 +567,7 @@ class DecisionTreeClassifier(BaseDecisionTree, ClassifierMixin):
         The number of features to consider when looking for the best split:
 
             - If int, then consider `max_features` features at each split.
-            - If float, then `max_features` is a percentage and
+            - If float, then `max_features` is a fraction and
               `int(max_features * n_features)` features are considered at each
               split.
             - If "auto", then `max_features=sqrt(n_features)`.
@@ -905,23 +905,23 @@ class DecisionTreeRegressor(BaseDecisionTree, RegressorMixin):
         The minimum number of samples required to split an internal node:
 
         - If int, then consider `min_samples_split` as the minimum number.
-        - If float, then `min_samples_split` is a percentage and
+        - If float, then `min_samples_split` is a fraction and
           `ceil(min_samples_split * n_samples)` are the minimum
           number of samples for each split.
 
         .. versionchanged:: 0.18
-           Added float values for percentages.
+           Added float values for fractions.
 
     min_samples_leaf : int, float, optional (default=1)
         The minimum number of samples required to be at a leaf node:
 
         - If int, then consider `min_samples_leaf` as the minimum number.
-        - If float, then `min_samples_leaf` is a percentage and
+        - If float, then `min_samples_leaf` is a fraction and
           `ceil(min_samples_leaf * n_samples)` are the minimum
           number of samples for each node.
 
         .. versionchanged:: 0.18
-           Added float values for percentages.
+           Added float values for fractions.
 
     min_weight_fraction_leaf : float, optional (default=0.)
         The minimum weighted fraction of the sum total of weights (of all
@@ -932,7 +932,7 @@ class DecisionTreeRegressor(BaseDecisionTree, RegressorMixin):
         The number of features to consider when looking for the best split:
 
         - If int, then consider `max_features` features at each split.
-        - If float, then `max_features` is a percentage and
+        - If float, then `max_features` is a fraction and
           `int(max_features * n_features)` features are considered at each
           split.
         - If "auto", then `max_features=n_features`.
@@ -1160,23 +1160,23 @@ class ExtraTreeClassifier(DecisionTreeClassifier):
         The minimum number of samples required to split an internal node:
 
         - If int, then consider `min_samples_split` as the minimum number.
-        - If float, then `min_samples_split` is a percentage and
+        - If float, then `min_samples_split` is a fraction and
           `ceil(min_samples_split * n_samples)` are the minimum
           number of samples for each split.
 
         .. versionchanged:: 0.18
-           Added float values for percentages.
+           Added float values for fractions.
 
     min_samples_leaf : int, float, optional (default=1)
         The minimum number of samples required to be at a leaf node:
 
         - If int, then consider `min_samples_leaf` as the minimum number.
-        - If float, then `min_samples_leaf` is a percentage and
+        - If float, then `min_samples_leaf` is a fraction and
           `ceil(min_samples_leaf * n_samples)` are the minimum
           number of samples for each node.
 
         .. versionchanged:: 0.18
-           Added float values for percentages.
+           Added float values for fractions.
 
     min_weight_fraction_leaf : float, optional (default=0.)
         The minimum weighted fraction of the sum total of weights (of all
@@ -1187,7 +1187,7 @@ class ExtraTreeClassifier(DecisionTreeClassifier):
         The number of features to consider when looking for the best split:
 
             - If int, then consider `max_features` features at each split.
-            - If float, then `max_features` is a percentage and
+            - If float, then `max_features` is a fraction and
               `int(max_features * n_features)` features are considered at each
               split.
             - If "auto", then `max_features=sqrt(n_features)`.
@@ -1343,23 +1343,23 @@ class ExtraTreeRegressor(DecisionTreeRegressor):
         The minimum number of samples required to split an internal node:
 
         - If int, then consider `min_samples_split` as the minimum number.
-        - If float, then `min_samples_split` is a percentage and
+        - If float, then `min_samples_split` is a fraction and
           `ceil(min_samples_split * n_samples)` are the minimum
           number of samples for each split.
 
         .. versionchanged:: 0.18
-           Added float values for percentages.
+           Added float values for fractions.
 
     min_samples_leaf : int, float, optional (default=1)
         The minimum number of samples required to be at a leaf node:
 
         - If int, then consider `min_samples_leaf` as the minimum number.
-        - If float, then `min_samples_leaf` is a percentage and
+        - If float, then `min_samples_leaf` is a fraction and
           `ceil(min_samples_leaf * n_samples)` are the minimum
           number of samples for each node.
 
         .. versionchanged:: 0.18
-           Added float values for percentages.
+           Added float values for fractions.
 
     min_weight_fraction_leaf : float, optional (default=0.)
         The minimum weighted fraction of the sum total of weights (of all
@@ -1370,7 +1370,7 @@ class ExtraTreeRegressor(DecisionTreeRegressor):
         The number of features to consider when looking for the best split:
 
         - If int, then consider `max_features` features at each split.
-        - If float, then `max_features` is a percentage and
+        - If float, then `max_features` is a fraction and
           `int(max_features * n_features)` features are considered at each
           split.
         - If "auto", then `max_features=n_features`.