########################### 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: