1########################### 2Regression (``regression``) 3########################### 4 5.. automodule:: Orange.regression 6 7 8.. index:: .. index:: linear fitter 9 pair: regression; linear fitter 10 11Linear Regression 12----------------- 13 14Linear regression is a statistical regression method which tries to 15predict a value of a continuous response (class) variable based on 16the values of several predictors. The model assumes that the response 17variable is a linear combination of the predictors, the task of 18linear regression is therefore to fit the unknown coefficients. 19 20 21Example 22======= 23 24 >>> from Orange.regression.linear import LinearRegressionLearner 25 >>> mpg = Orange.data.Table('auto-mpg') 26 >>> mean_ = LinearRegressionLearner() 27 >>> model = mean_(mpg[40:110]) 28 >>> print(model) 29 LinearModel LinearRegression(copy_X=True, fit_intercept=True, normalize=False) 30 >>> mpg[20] 31 Value('mpg', 25.0) 32 >>> model(mpg[0]) 33 Value('mpg', 24.6) 34 35.. autoclass:: Orange.regression.linear.LinearRegressionLearner 36.. autoclass:: Orange.regression.linear.RidgeRegressionLearner 37.. autoclass:: Orange.regression.linear.LassoRegressionLearner 38.. autoclass:: Orange.regression.linear.SGDRegressionLearner 39.. autoclass:: Orange.regression.linear.LinearModel 40 41 42 43.. index:: mean fitter 44 pair: regression; mean fitter 45 46 47Polynomial 48---------- 49 50*Polynomial model* is a wrapper that constructs polynomial features of 51a specified degree and learns a model on them. 52 53.. autoclass:: Orange.regression.linear.PolynomialLearner 54 55 56Mean 57---- 58 59*Mean model* predicts the same value (usually the distribution mean) for all 60data instances. Its accuracy can serve as a baseline for other regression 61models. 62 63The model learner (:class:`MeanLearner`) computes the mean of the given data or 64distribution. The model is stored as an instance of :class:`MeanModel`. 65 66Example 67======= 68 69 >>> from Orange.data import Table 70 >>> from Orange.regression import MeanLearner 71 >>> data = Table('auto-mpg') 72 >>> learner = MeanLearner() 73 >>> model = learner(data) 74 >>> print(model) 75 MeanModel(23.51457286432161) 76 >>> model(data[:4]) 77 array([ 23.51457286, 23.51457286, 23.51457286, 23.51457286]) 78 79.. autoclass:: MeanLearner 80 :members: 81 82 83 84.. index:: random forest 85 pair: regression; random forest 86 87Random Forest 88------------- 89.. autoclass:: RandomForestRegressionLearner 90 :members: 91 92 93 94.. index:: random forest (simple) 95 pair: regression; simple random forest 96 97Simple Random Forest 98-------------------- 99 100.. autoclass:: SimpleRandomForestLearner 101 :members: 102 103 104 105.. index:: regression tree 106 pair: regression; tree 107 108Regression Tree 109------------------- 110 111Orange includes two implemenations of regression tres: a home-grown one, and one 112from scikit-learn. The former properly handles multinominal and missing values, 113and the latter is faster. 114 115.. autoclass:: TreeLearner 116 :members: 117 118.. autoclass:: SklTreeRegressionLearner 119 :members: 120 121 122.. index:: neural network 123 pair: regression; neural network 124 125Neural Network 126-------------- 127.. autoclass:: NNRegressionLearner 128 :members: 129 130 131Gradient Boosted Trees 132---------------------- 133 134.. automodule:: Orange.regression.gb 135 136.. autoclass:: GBRegressor 137 :members: 138 139.. automodule:: Orange.regression.catgb 140 141.. autoclass:: CatGBRegressor 142 :members: 143 144.. automodule:: Orange.regression.xgb 145 146.. autoclass:: XGBRegressor 147 :members: 148 149.. autoclass:: XGBRFRegressor 150 :members: 151