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@mail4umar
mail4umar committed Oct 23, 2023
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Showing 1 changed file with 24 additions and 24 deletions.
48 changes: 24 additions & 24 deletions verticapy/machine_learning/vertica/tree.py
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Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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.
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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.
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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:
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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:
Expand Down

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