代码如下: summary=model.summaryprint(summary) 1. 2. 通过这段代码,我们可以获取线性回归模型的summary信息,包括参数估计、R方值等。 完整代码示例 下面是完整的代码示例: fromsklearn.datasetsimportload_bostonfromsklearn.linear_modelimportLinearRegression boston=load_boston()X=boston.data y=boston.target mod...
本篇文章采用调用机器学习库sklearn的方法来实现简单线性回归。 from sklearn.linear_model import LinearRegression #调库 lr=LinearRegression() #实例化对象 lr.fit(x,y) #fit()期望的是二维数组(矩阵) #目标函数y=w1x1+...+wnxn+b lr.coef_ #为w1~wn lr.intercept_ #为b sklearn中LogisticRegression...
但是现实中使用更多的是使用交叉验证来选择最佳的正则化系数:classsklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, scoring=None,cv=None, gcv_mode=None, store_cv_values=False) Ridge_ = RidgeCV(alphas=np.arange(1,1001,100),store_cv_values=True).fit(...
# Create linear regression object linear = linear_model.LinearRegression() # Train the model using the training sets and check score linear.fit(x_train, y_train) linear.score(x_train, y_train) #Equation coefficient and Intercept print('Coefficient: \n', linear.coef_) print('Intercept: \n...
importnumpyasnpfromsklearn.datasetsimportload_irisfromsklearn.linear_modelimportPerceptron iris = load_iris(as_frame=True) X = iris.data[["petal length (cm)","petal width (cm)"]].values y = (iris.target ==0)# Iris setosaper_clf = Perceptron(random_state=42) ...
model = LinearRegression() # 构建线性模型 model.fit(x, y) # 自变量在前,因变量在后 predicts = model.predict(x) # 预测值 R2 = model.score(x, y) # 拟合程度 R2 print('R2 = %.3f' % R2) # 输出 R2 coef = model.coef_ # 斜率 intercept = model.intercept_ # 截距 print(model.coef...
import plotly.graph_objects as gofrom sklearn.linear_model import LinearRegressionX = df.open.values.reshape(-1, 1)# 回归模型训练model = LinearRegression()model.fit(X, df.close)# 生产预测点x_range = np.linspace(X.min(), X.max(), 100)y_range = model.predict(x_range.reshape(-1, 1...
Deprecation Warning Numpy >=1.20 sklearn.linear_model.LinearRegression #51604 Sign in to view logs Summary Jobs one Run details Usage Workflow file Triggered via issue August 17, 2024 13:39 EwoutH commented on #21193 e87b32a Status Skipped Total duration 2s Artifacts – assign.yml ...
importshap# 训练随机森林模型model=RandomForestClassifier(n_estimators=100,random_state=42)model.fit(X_train,y_train)# 创建 SHAP 解释器explainer=shap.TreeExplainer(model)shap_values=explainer.shap_values(X_test)# 可视化 SHAP 值shap.summary_plot(shap_values,X_test,feature_names=iris.feature_names)...
model=LinearRegression()model.fit(X,df.close)# 生产预测点 x_range=np.linspace(X.min(),X.max(),100)y_range=model.predict(x_range.reshape(-1,1))# 图形绘制 fig=px.scatter(df,x='open',y='close',opacity=0.65)fig.add_traces(go.Scatter(x=x_range,y=y_range,name='Regression Fit')...