svr_rbf = SVR(kernel='rbf') stregr =StackingRegressor(regressors=[svr_lin, lr, ridge], meta_regressor=svr_rbf) y_pred = stregr.fit(X1, y).predict(X1) mse =0.214got = np.mean((stregr.predict(X1) - y) **2)assertround(got,3) == mse 开发者ID:datasci-co,项目名称:mlxtend,代码...
svr_rbf = SVR(kernel='rbf')#融合四个模型stregr = StackingRegressor(regressors=[svr_lin, lr, ridge], meta_regressor=svr_rbf)# 训练stacking分类器stregr.fit(X, y) stregr.predict(X)# 拟合结果的评估和可视化print("Mean Squared Error: %.4f"% np.mean((stregr.predict(X) - y) **2))pr...
fromsklearn.model_selectionimportGridSearchCVfromsklearn.linear_modelimportLasso# 初始化模型lr=LinearRegression()svr_lin=SVR(kernel='linear')ridge=Ridge(random_state=1)lasso=Lasso(random_state=1)svr_rbf=SVR(kernel='rbf')regressors=[svr_lin,lr,ridge,lasso]stregr=StackingRegressor(regressors=regresso...
# train regressor reg = RandomForestRegressor(10, min_samples_leaf=10, max_depth=9, n_jobs=-1) # reg = KNeighborsRegressor(algorithm="auto") # reg = LinearRegression() # reg = sklearn.svm.SVR(kernel="rbf", degree=3, C=100., gamma=10.) # reg =...
estimator = SVR(kernel=kernel)# try to change to the model for which the test is gonna run (lasso, ridge, etc.)X, y = make_friedman1(n_samples=1200, random_state=0, noise=1.0) X_train, X_test = X[:200], X[200:] y_train, y_test = y[:200], y[200:] ...
(self, data, target): from sklearn.svm.classes import SVR svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1) svr_rbf.fit(data, target) self.ensemble = svr_rbf def __random_forest_regressor__(self, data, target): from sklearn.model_selection import Randomized...
The parameters of Support Vector Regression (SVR) are kernel = 'rbf', degree = 3, coef = 0.0, tol = 0.001, C = 1.0, ε = 0.1. The parameters of BayesianRidge are n_iter = 300, tol = 0.001, α1 = 10−6, α2 = 10−6, λ1 = 10−6, λ2 = 10−6. 2.5. ...
The instance matrix used for training. -alpha: The output of MLSSVRTrain. -b: The output of MLSSVRTrain. -lambda: A positive real regularized parameter. It should be same as counterpart in MLSSVRTrain. -p: The positive hyperparameter of RBF kernel function. It should be same as counterpa...
svr_rbf = SVR(kernel='rbf')#融合四个模型stregr = StackingRegressor(regressors=[svr_lin, lr, ridge], meta_regressor=svr_rbf)# 训练stacking分类器stregr.fit(X, y) stregr.predict(X)# 拟合结果的评估和可视化print("Mean Squared Error: %.4f"% np.mean((stregr.predict(X) - y) **2))pr...
()svr_lin=SVR(kernel='linear')ridge=Ridge(random_state=1)svr_rbf=SVR(kernel='rbf')#融合四个模型stregr=StackingRegressor(regressors=[svr_lin,lr,ridge],meta_regressor=svr_rbf)# 训练stacking分类器stregr.fit(X,y)stregr.predict(X)# 拟合结果的评估和可视化print("Mean Squared Error: %.4f"%...