R_squared = 1 - SSE / SST 1. 实现代码示例 下面是使用Python实现回归计算R方的示例代码: importnumpyasnpdefcalculate_r_squared(y,y_hat):sse=np.sum((y-y_hat)**2)sst=np.sum((y-np.mean(y))**2)r_squared=1-sse/sstreturnr_squared# 生成示例数据np.random.seed(0)x=np.random.rand(10...
然后,我们可以计算R方值: # 计算R方值r_squared=model.score(X,y)print("R-squared:",r_squared) 1. 2. 3. 通过以上代码,我们可以得到拟合模型的R方值。在实际应用中,可以通过比较不同模型的R方值来评估它们的拟合优度,选择最合适的模型。 结语 R方是评估回归模型拟合优度的重要统计量之一,它可以帮助...
from sklearn.metrics import mean_squared_error # 计算均方误差(MSE) mse = mean_squared_error(y_true, y_pred) print(f"使用库计算的均方误差(MSE): {mse}") # 手动定义函数计算均方误差(MSE) def calculate_mse(y_true, y_pred): return np.mean((y_true - y_pred) ** 2) # 计算均方误差(...
'参数'[para]="pro_t_sys")pro_t_cus = CALCULATE(SUM([index]),'参数'[para]="pro_t_cus")GMV预测 = [截距] + [uv]*[UV Value] + [promotion_exp]*[费用投入 Value] + [price_diff]*[价格降低 Value] + [service_score]*[服务评分...
y) # 得分 score = 10000 * slope * r_squared return score # 斜率计算长度 N = 25 # 计算每日涨跌幅和得分 for name in name_list: data['日收益率_'+name] = data[name] / data[name].shift(1) - 1.0 data['得分_'+name] = data[name].rolling(N).apply(lambda x: calculate_score(x,...
def calculate_area(length, width): return length * width * 3.14 在这个代码中，3.14 是一个魔法数值，它表示圆周率。为了遵循“避免硬编码数值或字符串”的原则，我们可以将这个值定义为一个常量，并在代码中使用常量名称，如下所示：PI = 3.14def calculate_area(length, width): return length *...
(2))radius_squared = x**2+ y**2ifradius_squared <=1:within_circle +=1pi_estimate =4* within_circle / n_pointsifnotshow_estimate:print("Final Estimation of Pi=", pi_estimate)defrun_test(n_points: int,n_repeats: int,only_time: bool,)->No...
calculate_average([1, 2, 3, 4, 5]) 此装饰器将在调用calculate_average函数时自动记录日志。 3.2.1.2 性能分析装饰器 这里展示一个计算函数执行时间的装饰器: import time def timing_decorator(original_function): @functools.wraps(original_function) ...
= [x for x in train]# make predictionspredictions = list()for t in range(len(test)):model = ARIMA(history, order=arima_order)model_fit = model.fit(disp=0)yhat = model_fit.forecast()[0]predictions.append(yhat)history.append(test[t])# calculate out of sample errormse = mean_squared...
3random_forest_pearson_r=stats.pearsonr(test_Y,random_forest_predict) 4random_forest_R2=metrics.r2_score(test_Y,random_forest_predict) 5random_forest_RMSE=metrics.mean_squared_error(test_Y,random_forest_predict)**0.5 6print('Pearson correlation coefficient is {0}, and RMSE is {1}.'.forma...