Simple Linear Regression with Sklearn To demonstrate simple linear regression using the sklearn library, we'll use a California house price prediction dataset from Kaggle. Importing Libraries importpandasaspdfromsklearn.model_selectionimporttrain_test_splitfromsklearn.linear_modelimportLinearRegressionfromsk...
sklearn.metrics.roc_curve(y_ture,y_score,pos_label=None, sample_weight=None,drop_intermediate=True) 1. 2. import numpy as np from sklearn.metrics import roc_curve from sklearn.metrics import roc_auc_score y = np.array([1, 1, 2, 2]) scores = np.array([0.1, 0.4, 0.35, 0.8]) ...
from sklearn.linear_model import LogisticRegression from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier, RandomForestClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.cross_validation import cross_val_score 1. 2. 3. 4. 5. 6....
from sklearn import datasets boston = datasets.load_boston() # 载入boston房价模型 print(dir(boston),"\n",boston.data.shape,"\n",boston.target.shape) #查看模型描述, 特征值数量, 目标数量 from sklearn import linear_model linereg01= linear_model.LinearRegression() #生成一个线性回归实例 # 分...
from sklearn.linear_model import LinearRegression import numpy as np # Create a dataset x = np.array([5, 15, 25, 35, 45, 55]).reshape((-1, 1)) y = np.array([5, 20, 14, 32, 22, 38]) # Create a model …
然后，我们使用sklearn中的线性回归模型进行拟合和预测。# 导入线性回归模型from sklearn.linear_model import LinearRegression# 创建线性回归模型对象model = LinearRegression()# 在训练集上拟合模型model.fit(X_train, y_train)# 在测试集上进行预测y_pred = model.predict(X_test)print(y_pred.shape)print(y...
sklearn 的 LinearRegression模块 fromsklearn.linear_modelimportLinearRegression#导入LinearRegression模块(普通最小二乘线性回归)#LinearRegression 拟合线性模型,系数 w = (w1, …, wp) 最小化观察目标之间的残差平方和 数据集#以及线性近似预测的目标。LinearRegression(fit_intercept = True,normalize = False,...
The L2 norm term is weighted by a regularization parameter alpha: if alpha=0 then you recover the Ordinary Least Squares regression model. The larger the alpha the higher the smoothness constraint. Below you can see the approximation of a sklearn.linear_model.RidgeRegression estimator fitting a ...
>>> from sklearn import linear_model >>> clf = linear_model.LinearRegression() >>> clf.fit ([[0, 0], [1, 1], [2, 2]], [0, 1, 2]) LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False) >>> clf.coef_ array([ 0.5, 0.5]) 1 2 3 4 5 6...
feature_selection import RFE from sklearn.linear_model import LinearRegression def excel_one_line_to_list(): X = pd.read_excel("G:\毕业论文\B数据集\水稻稻叶瘟\data1.xls", usecols=[0,1,2,3,4,5],names=None) names = X.columns.tolist() X = np.array(X) X = X.tolist() Y =...