regression.addData(1, 2); regression.addData(2, 3); regression.addData(3, 5); regression.addData(4, 7); // 计算回归线 regression.regress(); // 输出结果 doubleslope = regression.getSlope();// 斜率 doubleintercept = regression.getIntercept();// 截距 doublerSquared = regression.getRSqu...
Linear regression r-squaredlinreg.results
plt.plot(xx, regressor_quadratic.predict(xx_quadratic),'r-') plt.show()print(X_train)print(X_train_quadratic)print(X_test)print(X_test_quadratic)print'一元线性回归 r-squared', regressor.score(X_test, y_test)print'二次回归 r-squared', regressor_quadratic.score(X_test_quadratic, y_test)...
Linear regression models have a special related measure called R2 (R-squared). R2 is a value between 0 and 1 that tells us how well a linear regression model fits the data. When people talk about correlations being strong, they often mean that the R2 value was large....
Visual Example of a High R - Squared Value (0.79) However, if we plotDurationandCalorie_Burnage, the R-Squared increases. Here, we see that the data points are close to the linear regression function line: Here is the code in Python: ...
R-squared(决定系数)衡量了模型对观测数据的拟合程度。它表示因变量方差中能被自变量解释的比例,取值范围在0到1之间。R-squared越接近1,说明模型对数据拟合得越好。 3. 参数影响 3.1 斜率参数影响 斜率参数决定了自变量对因变量的影响程度。当斜率为正时,自变量增加会导致因变量增加;当斜率为负时,自变量增加会导致因...
线性回归(Linear Regression) 是统计学和机器学习中最基础、最广泛使用的预测建模技术之一。它的基本思想是通过建立自变量(独立变量)和因变量(响应变量)之间的线性关系,来预测或解释因变量的变化。线性回归模型假设因变量是自变量的线性组合,再加上一个误差项。在线性回归中,我们试图找到最佳拟合线,即能够最小化实...
3. Regularized Linear Regression 在线性回归中,我们可以引入正则项(惩罚项)来防止过拟合现象,其中最有名气的两种是Ridge Regression 和 Lasso。它们一般的可以表示为如下优化问题: \begin{equation}\frac{1}{2} \|T - Xw\|_2^2 + \frac{\lambda}{2} \sum_{i=1}^D |w_i|^q\tag{53}\end{equation...
regressor = LinearRegression()regressor.fit(X_train, y_train)xx = np.linspace(0, 26, 100)yy = regressor.predict(xx.reshape(xx.shape[0], 1))plt = LRplt.runplt()plt.plot(X_train, y_train, 'k.')plt.plot(xx, yy)quadratic_featurizer = PolynomialFeatures(degree=2)X_train_quadratic ...