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###########################
Regression (``regression``)
###########################
.. automodule:: Orange.regression
.. index:: .. index:: linear fitter
pair: regression; linear fitter
Linear Regression
-----------------
Linear regression is a statistical regression method which tries to
predict a value of a continuous response (class) variable based on
the values of several predictors. The model assumes that the response
variable is a linear combination of the predictors, the task of
linear regression is therefore to fit the unknown coefficients.
Example
=======
>>> from Orange.regression.linear import LinearRegressionLearner
>>> mpg = Orange.data.Table('auto-mpg')
>>> mean_ = LinearRegressionLearner()
>>> model = mean_(mpg[40:110])
>>> print(model)
LinearModel LinearRegression(copy_X=True, fit_intercept=True, normalize=False)
>>> mpg[20]
Value('mpg', 25.0)
>>> model(mpg[0])
Value('mpg', 24.6)
.. autoclass:: Orange.regression.linear.LinearRegressionLearner
.. autoclass:: Orange.regression.linear.RidgeRegressionLearner
.. autoclass:: Orange.regression.linear.LassoRegressionLearner
.. autoclass:: Orange.regression.linear.SGDRegressionLearner
.. autoclass:: Orange.regression.linear.LinearModel
.. index:: mean fitter
pair: regression; mean fitter
Polynomial
----------
*Polynomial model* is a wrapper that constructs polynomial features of
a specified degree and learns a model on them.
.. autoclass:: Orange.regression.linear.PolynomialLearner
Mean
----
*Mean model* predicts the same value (usually the distribution mean) for all
data instances. Its accuracy can serve as a baseline for other regression
models.
The model learner (:class:`MeanLearner`) computes the mean of the given data or
distribution. The model is stored as an instance of :class:`MeanModel`.
Example
=======
>>> from Orange.data import Table
>>> from Orange.regression import MeanLearner
>>> data = Table('auto-mpg')
>>> learner = MeanLearner()
>>> model = learner(data)
>>> print(model)
MeanModel(23.51457286432161)
>>> model(data[:4])
array([ 23.51457286, 23.51457286, 23.51457286, 23.51457286])
.. autoclass:: MeanLearner
:members:
.. index:: random forest
pair: regression; random forest
Random Forest
-------------
.. autoclass:: RandomForestRegressionLearner
:members:
.. index:: random forest (simple)
pair: regression; simple random forest
Simple Random Forest
--------------------
.. autoclass:: SimpleRandomForestLearner
:members:
.. index:: regression tree
pair: regression; tree
Regression Tree
-------------------
Orange includes two implemenations of regression tres: a home-grown one, and one
from scikit-learn. The former properly handles multinominal and missing values,
and the latter is faster.
.. autoclass:: TreeLearner
:members:
.. autoclass:: SklTreeRegressionLearner
:members:
.. index:: neural network
pair: regression; neural network
Neural Network
--------------
.. autoclass:: NNRegressionLearner
:members:
Gradient Boosted Trees
----------------------
.. automodule:: Orange.regression.gb
.. autoclass:: GBRegressor
:members:
.. automodule:: Orange.regression.catgb
.. autoclass:: CatGBRegressor
:members:
.. automodule:: Orange.regression.xgb
.. autoclass:: XGBRegressor
:members:
.. autoclass:: XGBRFRegressor
:members:
Curve Fit
----------------------
.. automodule:: Orange.regression.curvefit
.. autoclass:: CurveFitLearner
:members:
|
