下面是一个线性回归的简单案例: fromsklearn.linear_model import LinearRegression import numpyasnp# Sample dataX = np.array([[1], [2], [3], [4], [5]])# Input featurey = np.array([2,3.5,2.8,4.6,5.2])# Output target# Create a linear regression modelmodel =LinearRegression()# Fit ...
删除文件 1-线性回归LinearRegression/SGDRegressor.joblib 2年前 LICENSE add LICENSE. 2年前 LinearRegression_2.py 采用线性回归模型对波士顿房价进行预测-sklearn 实现- 2年前 README.md update README.md. 2年前 image.png update README.md. 2年前 ...
print(__doc__)#Code source: Jaques Grobler#License: BSD 3 clauseimportmatplotlib.pyplot as pltimportnumpy as npfromsklearnimportdatasets, linear_modelfromsklearn.metricsimportmean_squared_error, r2_score#Load the diabetes datasetdiabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True)#U...
Estimator参数:所有的estimator参数可以在初始化时被赋值,否则会用本身的默认值。 model = LinearRegression(normalize=True) print(model.normalize) True print(model) LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=True) 估计模型的参数:当数据已经拟合了estimator,模型参数就能很方便的估...
print(__doc__)# Code source:Gaël Varoquaux # Modifiedfordocumentation by Jaques Grobler # License:BSD3clauseimportnumpyasnpimportmatplotlib.pyplotasplt from sklearn.linear_modelimportLogisticRegression from sklearnimportdatasets #importsome data to playwithiris=datasets.load_iris()X=iris.data[:,...
from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.metrics import classification_report # 地址路径 names = ["Sample code number ","Clump Thickness","Uniformity of Cell Size","Uniformity ...
例如,上一章的LinearRegression模型就是一个预测器:它根据一个国家的人均 GDP 预测生活满意度。预测器有一个predict()方法,可以用新实例的数据集做出相应的预测。预测器还有一个score()方法,可以根据测试集(和相应的标签,如果是监督学习算法的话)对预测进行衡器。 可检验。所有估计器的超参数都可以通过实例的public...
X_test=sc_X.transform(SourceData_test_independent) y_test=SourceData_test_dependent 第5步-现在我们将分别输入独立和因变量数据,即X_train 和y_train ,以训练线性回归模型。出于本文开头提到的原因,我们将使用默认参数执行模型拟合。 reg = LinearRegression().fit(X_train, y_train) ...
from cuml.metrics.regression import r2_score from sklearn.linear_model import LinearRegression as skLinearRegression 创建虚拟数据并将其拆分(训练和测试) n_samples = 2**20 n_features = 399 random_state = 23 X, y = make_regression(n_samples=n_samples, n_features=n_features, random_state=ran...
2. Criterion - sklearn source code In sklearn, following criterion class for regression and classification are defined. Criterion Class calculates the impurity of node and the reduction of impurity after split. I extract the source code related to the criterion calculation. ...