From 73af280c4c9f111d7c988b9c017e04504c30cca6 Mon Sep 17 00:00:00 2001 From: umar <46414488+mail4umar@users.noreply.github.com> Date: Mon, 23 Oct 2023 15:07:24 -0500 Subject: [PATCH] fixed black --- verticapy/machine_learning/vertica/tree.py | 48 +++++++++++----------- 1 file changed, 24 insertions(+), 24 deletions(-) diff --git a/verticapy/machine_learning/vertica/tree.py b/verticapy/machine_learning/vertica/tree.py index 542a742be..10d78b391 100755 --- a/verticapy/machine_learning/vertica/tree.py +++ b/verticapy/machine_learning/vertica/tree.py @@ -84,8 +84,8 @@ class DecisionTreeRegressor(RandomForestRegressor): .. important:: - Many tree-based models inherit from the ``RandomForest`` - base class, and it's recommended to use it directly for + Many tree-based models inherit from the ``RandomForest`` + base class, and it's recommended to use it directly for access to a wider range of options. Load data for machine learning @@ -243,9 +243,9 @@ class DecisionTreeRegressor(RandomForestRegressor): .. note:: - In models such as ``RandomForest``, feature importance is calculated - using the MDI (Mean Decreased Impurity). To determine the final score, - VerticaPy sums the scores of each tree, normalizes them and applies an + In models such as ``RandomForest``, feature importance is calculated + using the MDI (Mean Decreased Impurity). To determine the final score, + VerticaPy sums the scores of each tree, normalizes them and applies an activation function to scale them. Metrics @@ -275,7 +275,7 @@ class DecisionTreeRegressor(RandomForestRegressor): report can help optimize performance. E.g. ``model.report(metrics = ["mse", "r2"])``. - You can utilize the + You can utilize the :py:mod:`verticapy.machine_learning.vertica.ensemble.RandomForestRegressor.score` function to calculate various regression metrics, with the R-squared being the default. @@ -472,8 +472,8 @@ class DummyTreeRegressor(RandomForestRegressor): .. important:: - Many tree-based models inherit from the ``RandomForest`` - base class, and it's recommended to use it directly for + Many tree-based models inherit from the ``RandomForest`` + base class, and it's recommended to use it directly for access to a wider range of options. Load data for machine learning @@ -622,9 +622,9 @@ class DummyTreeRegressor(RandomForestRegressor): .. note:: - In models such as ``RandomForest``, feature importance is calculated - using the MDI (Mean Decreased Impurity). To determine the final score, - VerticaPy sums the scores of each tree, normalizes them and applies an + In models such as ``RandomForest``, feature importance is calculated + using the MDI (Mean Decreased Impurity). To determine the final score, + VerticaPy sums the scores of each tree, normalizes them and applies an activation function to scale them. Metrics @@ -654,7 +654,7 @@ class DummyTreeRegressor(RandomForestRegressor): report can help optimize performance. E.g. ``model.report(metrics = ["mse", "r2"])``. - You can utilize the + You can utilize the :py:mod:`verticapy.machine_learning.vertica.ensemble.RandomForestRegressor.score` function to calculate various regression metrics, with the R-squared being the default. @@ -873,8 +873,8 @@ class DecisionTreeClassifier(RandomForestClassifier): .. important:: - Many tree-based models inherit from the ``RandomForest`` - base class, and it's recommended to use it directly for + Many tree-based models inherit from the ``RandomForest`` + base class, and it's recommended to use it directly for access to a wider range of options. Load data for machine learning @@ -1036,9 +1036,9 @@ class DecisionTreeClassifier(RandomForestClassifier): .. note:: - In models such as ``RandomForest``, feature importance is calculated - using the MDI (Mean Decreased Impurity). To determine the final score, - VerticaPy sums the scores of each tree, normalizes them and applies an + In models such as ``RandomForest``, feature importance is calculated + using the MDI (Mean Decreased Impurity). To determine the final score, + VerticaPy sums the scores of each tree, normalizes them and applies an activation function to scale them. Metrics @@ -1087,7 +1087,7 @@ class DecisionTreeClassifier(RandomForestClassifier): :file: SPHINX_DIRECTORY/figures/machine_learning_vertica_dtreeclass_report_cutoff.html - You can also use the + You can also use the :py:mod:`verticapy.machine_learning.vertica.ensemble.RandomForestClassifier.score` function to compute any classification metric. The default metric is the accuracy: @@ -1410,8 +1410,8 @@ class DummyTreeClassifier(RandomForestClassifier): .. important:: - Many tree-based models inherit from the ``RandomForest`` - base class, and it's recommended to use it directly for + Many tree-based models inherit from the ``RandomForest`` + base class, and it's recommended to use it directly for access to a wider range of options. Load data for machine learning @@ -1567,9 +1567,9 @@ class DummyTreeClassifier(RandomForestClassifier): .. note:: - In models such as ``RandomForest``, feature importance is calculated - using the MDI (Mean Decreased Impurity). To determine the final score, - VerticaPy sums the scores of each tree, normalizes them and applies an + In models such as ``RandomForest``, feature importance is calculated + using the MDI (Mean Decreased Impurity). To determine the final score, + VerticaPy sums the scores of each tree, normalizes them and applies an activation function to scale them. Metrics @@ -1618,7 +1618,7 @@ class DummyTreeClassifier(RandomForestClassifier): :file: SPHINX_DIRECTORY/figures/machine_learning_vertica_dummytreecl_report_cutoff.html - You can also use the + You can also use the :py:mod:`verticapy.machine_learning.vertica.ensemble.RandomForestClassifier.score` function to compute any classification metric. The default metric is the accuracy: