可以借助train_test_split函数分割很方便: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html 代码解读 from sklearn.model_selection import train_test_split xtrain, xtest, ytrain, ytest = train_test_split(x_iris, y_iris, random_state=1) # 对数据...
用法: classsklearn.linear_model.SGDRegressor(loss='squared_error', *, penalty='l2', alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=1000, tol=0.001, shuffle=True, verbose=0, epsilon=0.1, random_state=None, learning_rate='invscaling', eta0=0.01, power_t=0.25, early_stopping=...
print ('The value of default measurement of LinearRegression is', lr.score(X_test, y_test)) #从sklearn.metrics依次导入r2_score、mean_squared_error以及mean_absoluate_error用于回归性能的评估。 from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error # 使用r2_score模块,并...
sklearn例程: 回归模型预测耗时对比 @丹阳▣技术教程♢Python,RandomForestRegressor,SGDRegressor,sklearn,SVR,例程,模型耗时 示例简介 此示例显示了几个scikit-learn回归模型预测耗费时间的对比,包括: SGDRegressor RandomForestRegressor SVR 具体来说...
from sklearn.linear_model import SGDRegressor # 使用默认配置初始化线性回归器SGDRegressor。 sgdr = SGDRegressor() # 使用训练数据进行参数估计。 sgdr.fit(X_train, y_train) # 对测试数据进行回归预测。 sgdr_y_predict = sgdr.predict(X_test)
fromsklearn.metricsimportmean_squared_error # 加载数据集 data=load_boston() X, y=data.data, data.target # 划分训练集和测试集 X_train, X_test, y_train, y_test=train_test_split(X, y, test_size=0.2, random_state=42) # 创建模型对象并设置参数 model=SGDRegressor(loss='squared_loss', ...
from sklearn.linear_model import SGDRegressor __author__ = 'zhen' X = 2 * np.random.rand(100, 1) y = 4 + 3 * X + np.random.randn(100, 1) # 梯度下降回归 sgd_reg = SGDRegressor(max_iter=100) # 最大迭代次数 sgd_reg.fit(X, y.ravel()) ...
importnumpyasnpfromsklearn.linear_modelimportLasso,Ridge,SGDRegressoralpha=0.1m=100np.random.seed(42)X=2*np.random.rand(m,1)y=4+3*X+np.random.randn(m,1)ridge=Ridge(alpha=alpha)ridge.fit(X,y)print("Ridge:",ridge.intercept_,ridge.coef_)sgd_reg=SGDRegressor(loss="squared_error",penalty...
Python之SGDRegressor 实现：# -*- coding: UTF-8 -*- import numpy as np from sklearn.linear_model import SGDRegressor __author__ = 'zhen'X = 2 * np.random.rand(100, 1)y = 4 + 3 * X + np.random.randn(100, 1)# 梯度下降回归 sgd_reg = SGDRegressor(max_iter=100) # 最⼤...
# 从sklearn.linear_model导⼊LinearRegression。from sklearn.linear_model import LinearRegression # 使⽤默认配置初始化线性回归器LinearRegression。lr = LinearRegression()# 使⽤训练数据进⾏参数估计。lr.fit(X_train, y_train)# 对测试数据进⾏回归预测。lr_y_predict = lr.predict(X_test)