在scikit中根据数据运行所有的回归器。

Question

我正在创建一个框架，在这个框架中，我可以调用scikit-learn中所有可用的回归器。与此相关，我有两个问题

1. 如何通过编程获取所有回归器的列表？
2. 目的是针对数据集运行回归器，并获取RMSE、R-Sq、Adjusted R-Sq等指标进行模型比较，然后应用超参数调整，再重新运行。

我正试图在Pyth-中复制这个功能。

https:/github.comtobigithubcaret-machine-learningblobmastercaret-regressioncaret-all-regression-models.R。

我相信这可以在scikit中完成。将感谢任何起点。

提前感谢。

Answer 1

我找不到一个程序化的方法来列出sklearn中的所有回归器，我已经导入了所有的回归器，然后在其中循环。

from sklearn.ensemble.forest import RandomForestRegressor
from sklearn.ensemble.forest import ExtraTreesRegressor
from sklearn.ensemble.bagging import BaggingRegressor
from sklearn.ensemble.gradient_boosting import GradientBoostingRegressor
from sklearn.ensemble.weight_boosting import AdaBoostRegressor
from sklearn.gaussian_process.gpr import GaussianProcessRegressor
from  sklearn.isotonic import IsotonicRegression
from sklearn.linear_model.bayes import ARDRegression
from sklearn.linear_model.huber import HuberRegressor
from sklearn.linear_model.base import LinearRegression
from sklearn.linear_model.passive_aggressive import PassiveAggressiveRegressor 
from sklearn.linear_model.randomized_l1 import RandomizedLogisticRegression
from sklearn.linear_model.stochastic_gradient import SGDRegressor
from sklearn.linear_model.theil_sen import TheilSenRegressor
from sklearn.linear_model.ransac import RANSACRegressor
from sklearn.multioutput import MultiOutputRegressor
from sklearn.neighbors.regression import KNeighborsRegressor
from sklearn.neighbors.regression import RadiusNeighborsRegressor
from sklearn.neural_network.multilayer_perceptron import MLPRegressor
from sklearn.tree.tree import DecisionTreeRegressor
from sklearn.tree.tree import ExtraTreeRegressor
from sklearn.svm.classes import SVR
from sklearn.linear_model import BayesianRidge
from sklearn.cross_decomposition import CCA
from sklearn.linear_model import ElasticNet
from sklearn.linear_model import ElasticNetCV
from sklearn.kernel_ridge import KernelRidge
from sklearn.linear_model import Lars
from sklearn.linear_model import LarsCV
from sklearn.linear_model import Lasso
from sklearn.linear_model import LassoCV
from sklearn.linear_model import LassoLars
from sklearn.linear_model import LassoLarsIC
from sklearn.linear_model import LassoLarsCV
from sklearn.linear_model import MultiTaskElasticNet
from sklearn.linear_model import MultiTaskElasticNetCV
from sklearn.linear_model import MultiTaskLasso
from sklearn.linear_model import MultiTaskLassoCV
from sklearn.svm import NuSVR
from sklearn.linear_model import OrthogonalMatchingPursuit
from sklearn.linear_model import OrthogonalMatchingPursuitCV
from sklearn.cross_decomposition import PLSCanonical
from sklearn.cross_decomposition import PLSRegression
from sklearn.linear_model import Ridge
from sklearn.linear_model import RidgeCV
from sklearn.svm import LinearSVR

然后，你可以通过他们循环精度= [] 。

for i in range(len(Name)):
    regressor = globals()[Name[i]]

    Regressor = regressor(**param[i])
    Regressor.fit(X_train, y_train)
    y_pred = Regressor.predict(X_test)
    from sklearn.metrics import mean_squared_error
    import numpy as np
    Nans = np.isnan(y_pred)
    y_pred[Nans] = 0
    accuracy.append(np.sqrt(mean_squared_error(y_pred,y_test)))

你需要将回归者的名字放入一个名字列表中，例如：你需要将回归者的名字放入一个名字列表中。

Name=[
'ExtraTreesRegressor',
'RandomForestRegressor']