model = LinearRegression() # 拟合模型 model.fit(x, y) # 输出模型的参数 print(f"斜率 (w): {model.coef_[0][0]}") print(f"截距 (b): {model.intercept_[0]}") # 预测 y_pred = model.predict(x) # 可视化拟合结果 plt.scatter(x, y) plt.plot(x, y_pred, color='red') plt.xla...
plotAdded(mdl) Create Scatter Plot for Simple Linear Regression Create a scatter plot of data along with a fitted curve and confidence bounds for a simple linear regression model. A simple linear regression model includes only one predictor variable. ...
plt.show() 1. 2. plot函数: kind='参数'表示的含义 ‘scatter’ : scatter plot#散点图。需指定X轴Y轴 'line’ : line plot (default)#折线图 ‘bar’ : vertical bar plot#条形图。 ‘hist’ : histogram#直方图 ‘box’ : boxplot#箱型图 ‘kde’ : Kernel Density Estimation plot#密度图 ‘h...
sns.scatterplot(x="area", y="price", data=transformed,ax=ax) sns.regplot(x="x", y="y", data=line,color='r') plt.show() 多元线性回归(Multiple linear regression) 上面示例是简单线性回归模型中,我们只使用一个输入值来预测目标值。这种简单性使得数据可视化和模型实现变得容易。然而,简单线性回归...
# Scatter plot plt.title("Generated data") plt.scatter(x=df["X"], y=df["y"]) plt.show() Split data 现在我们已经准备好了数据,接下来需要随机将数据集分成三个部分:训练集、验证集和测试集。 训练集:用来训练我们的模型 验证集:在训练过程中用来检验我们模型的性能 测试集:用来检测我们最终训练好...
plt.scatter(X_train, y_train, alpha = 0.6) #重新训练数据 linear = LinearRegression() linear.fit(X_train,y_train) #测试数据 xmin, xmax = X_train.min(), X_train.max() x_test = np.linspace(xmin, xmax,100).reshape((-1,1)) ...
Scatter plot with linear regression line showing the correlation between sea turtle body size and blood lactate levels.Gregory A. LewbartMaximilian HirschfeldJudith DenkingerKarla VascoNataly GuevaraJuan GarcíaJuanpablo MuñozKenneth J. Lohmann
The new residuals plot looks fairly symmetric, without obvious problems. However, there might be some serial correlation among the residuals. Create a new plot to see if such an effect exists. Get plotResiduals(mdl3,'lagged') The scatter plot shows many more crosses in the upper-right and ...
()# 绘图,每迭代20次就绘图ifiteration%20==0:# 真实值用散点图绘制plt.scatter(x.data.numpy(),y.data.numpy())# 预测的直线:"g-"表示绿色实线;lw表示线宽plt.plot(x.data.numpy(),y_pred.data.numpy(),"g-",lw=5)# 文本输出:(2,20)表示显示的坐标;size表示字体大小;color表示字体颜色plt....
plt.scatter(car_prices_array,number_of_car_sell_array) plt.xlabel("Car Price $") plt.ylabel("Number of Car Sell") plt.title("Car Price$ VS Number of Car Sell") plt.show() 结果: 第三步:构建LinearRegression类 1 2 3 4 5