1.2 R2求解方式二---从模型调用score r2 = linear.score(x_test,y_test) r2 1. 2. 0.5439247940652986 1. 1.3 R2求解方式二---交叉验证调用scoring=r2 from sklearn.model_selection import cross_val_score r2 = cross_val_score(linear,x_test,y_test,cv=10,scoring="r2").mean() # 求的值n次交...
R2( Coefficient of determination):决定系数,反映的是模型的拟合程度,R2的范围是0到1。其值越接近1,表明方程的变量对y的解释能力越强,这个模型对数据拟合的也较好。 1.1 R2求解方式一---从metrics调用r2_socre from sklearn.metrics import r2_scorer2 = r2_score(y_true=y_test,y_pred=y_pre)r2 0.5439...
python中可以直接调用 from sklearn.metrics import mean_squared_error #均方误差 from sklearn.metrics import mean_absolute_error #平方绝对误差 from sklearn.metrics import r2_score#R square #调用 MSE:mean_squared_error(y_test,y_predict) RMSE:np.sqrt(mean_squared_error(y_test,y_predict)) MAE:m...
function in sklearn. Thus I quickly made my own adjusted R square function. I am sharing my function with you. please add adjusted R square function when you update the version: def adj_r2_score(model,y,yhat): """Adjusted R square — put fitted linear model, y value, estimated y val...
)assertspecifics == [metrics.adjusted_mutual_info_score, metrics.r2_score] 开发者ID:equinor,项目名称:gordo,代码行数:18,代码来源:test_builder.py 示例2: evaluate_groups ▲点赞 6▼ # 需要导入模块: from sklearn import metrics [as 别名]# 或者: from sklearn.metrics importadjusted_mutual_info_...
We’ll then create a function named my_r2_score() that computes the R-squared of the model.import numpy as np import pandas as pd from sklearn.linear_model import LinearRegression from sklearn.metrics import r2_score from sklearn.datasets import load_boston X = load_boston()['data']....
本文简要介绍python语言中 sklearn.metrics.adjusted_mutual_info_score 的用法。 用法: sklearn.metrics.adjusted_mutual_info_score(labels_true, labels_pred, *, average_method='arithmetic') 两个聚类之间的调整互信息。 调整后的互信息 (AMI) 是对互信息 (MI) 分数的调整,以考虑机会。它解释了这样一个...