Test RMSE: 8.099

RMSE_SPEC.m.zip_RMSE_RMSE.m文件_statistic _wave

statistic analysis for wave spectral, RMSE, BIAS and Correlation

Matlab程序用于求RMSE.m.zip

music_rmse_snr.m

不同信噪比下的RMSE比较

Smoothing spline: f(x) = piecewise polynomial computed from p Smoothing parameter: p = 0.96732233 Goodness of fit: SSE: 0.01478 R-square: 0.998 Adjusted R-square: 0.9943 RMSE: 0.0695 解释下

平滑样条是一种用于拟合数据的方法，它使用多项式来逼近数据点。在平滑样条中，数据被分段逼近，每个段都由一个多项式来表示。...- 均方根误差（RMSE）：表示拟合曲线与原始数据点之间的平均误差，数值越小越好。

Smoothing spline: f(x) = piecewise polynomial computed from p Smoothing parameter: p = 0.96732233 Goodness of fit: SSE: 0.01478 R-square: 0.998 Adjusted R-square: 0.9943 RMSE: 0.0695给出公式

平滑样条的公式可以表示为： ...- 均方根误差（RMSE）：表示拟合曲线与原始数据点之间的平均误差，数值越小越好。以上是平滑样条的基本公式和评估指标。具体的计算方法可以根据具体的算法和实现进行调整。

f(x) = aexp(bx) Coefficients (with 95% confidence bounds): a = 0.003231 (0.002873, 0.003589) b = 0.03553 (0.0351, 0.03595) Goodness of fit: SSE: 0.4229 R-square: 0.9998 Adjusted R-square: 0.9998 RMSE: 0.2056 可以解释一下上述的代码吗

此外，还提供了拟合的好度量指标，如SSE（平方和误差）、R-square（确定系数）、调整后的R-square和RMSE（均方根误差）。请注意，上述代码仅用于指数拟合的示例，如果你想进行其他类型的拟合，需要相应地修改拟合...

优化代码： plt.title(f"The predicted values of test samples in LinearRegression\nRMSE = {rmse:.2f}, " f"estimator.coef = {estimator.coef:.2f}%" f"estimator.intercept = {estimator.intercept:.2f}%")

可以将代码进行分行，使代码更易读，同时在格式化...title += "RMSE = {:.2f}, estimator.coef = {:.2f}%, estimator.intercept = {:.2f}%".format(rmse, estimator.coef, estimator.intercept) plt.title(title)

title += "RMSE = {:.2f}, estimator.coef_ = {:.2f}%, {:.2f}%, {:.2f}%, {:.2f}%, estimator.intercept_ = {:.2f}%".format(rmse, estimator.coef_[0][1][2][3], estimator.intercept_) IndexError: invalid index to scalar variable.

title += "RMSE = {:.2f}, estimator.coef_ = {:.2f}%, {:.2f}%, {:.2f}%, {:.2f}%, estimator.intercept_ = {:.2f}%".format(rmse, estimator.coef_[0][1][2][3], estimator.intercept_[0], estimator.intercept_[1...

title += "RMSE = {:.2f}, estimator.coef_ = {:.2f}%, estimator.intercept_ = {:.2f}%, {:.2f}%, {:.2f}%,{:.2f}%".format(rmse, estimator.coef_[0][1][2][3], estimator.intercept_) IndexError: invalid index to scalar variable.

title += "RMSE = {:.2f}, estimator.coef_ = {:.2f}%, estimator.intercept_ = {:.2f}%, {:.2f}%, {:.2f}%, {:.2f}%".format(rmse, estimator.coef_[0][1][2][3], estimator.intercept_[0], estimator.intercept_[1...

unction load_boston is deprecated; load_boston is deprecated in 1.0 and will be removed in 1.2. The Boston housing prices dataset has an ethical problem. You can refer to the documentation of this function for further details. The scikit-learn maintainers therefore strongly discourage the use of this dataset unless the purpose of the code is to study and educate about ethical issues in data science and machine learning. In this special case, you can fetch the dataset from the original source:: import pandas as pd import numpy as np data_url = "http://lib.stat.cmu.edu/datasets/boston" raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None) data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]]) target = raw_df.values[1::2, 2] Alternative datasets include the California housing dataset (i.e. :func:~sklearn.datasets.fetch_california_housing) and the Ames housing dataset. You can load the datasets as follows:: from sklearn.datasets import fetch_california_housing housing = fetch_california_housing() for the California housing dataset and:: from sklearn.datasets import fetch_openml housing = fetch_openml(name="house_prices", as_frame=True) for the Ames housing dataset. warnings.warn(msg, category=FutureWarning) 线性回归模型的RMSE: 4.928602182665355 决策树回归模型的RMSE: 3.2273949915330395 随机森林回归模型的RMSE: 2.8109631609391226

这段提示信息的意思是，函数load_boston已经被弃用了，在1.0版本中已经弃用，在1.2版本中将被删除。波士顿房价数据集存在伦理问题，因此强烈不建议在非研究和教育伦理问题的情况下...最后，给出了三种模型的RMSE值。

OLS Regression Results Dep. Variable: count R-squared: 0.156 Model: OLS Adj. R-squared: 0.156 Method: Least Squares F-statistic: 2006. Date: Sat, 03 Jun 2023 Prob (F-statistic): 0.00 Time: 13:53:24 Log-Likelihood: -71125. No. Observations: 10886 AIC: 1.423e+05 Df Residuals: 10884 BIC: 1.423e+05 Df Model: 1 Covariance Type: nonrobust coef std err t P>|t| [0.025 0.975] const 6.0462 4.439 1.362 0.173 -2.656 14.748 temp 9.1705 0.205 44.783 0.000 8.769 9.572 Omnibus: 1871.687 Durbin-Watson: 0.369 Prob(Omnibus): 0.000 Jarque-Bera (JB): 3221.966 Skew: 1.123 Prob(JB): 0.00 Kurtosis: 4.434 Cond. No. 60.4请帮我详细分析这个表格，并对模型做出详细的解释frmse为161.62822792768694，l为161.62822792768694

模型的 RMSE（均方根误差）为 161.63，表示模型的平均预测误差为 161.63。同时，l 的值也为 161.63，可能是你在表格中标注的一个符号。但是，我并不清楚它的含义和作用。希望我的解释能对你有所帮助。

注释以下代码 def k_fold(k, X_train, y_train, num_epochs, learning_rate, weight_decay, batch_size): train_l_sum, valid_l_sum = 0.0, 0.0 for i in range(k): data = get_k_fold_data(k, i, X_train, y_train) train_ls, valid_ls = train(model, *data, num_epochs, learning_rate, weight_decay, batch_size) train_l_sum += train_ls[-1] valid_l_sum += valid_ls[-1] print("fold %d, train rmse: %.4f, valid rmse: %.4f" % (i, train_ls[-1], valid_ls[-1])) return train_l_sum / k, valid_l_sum / k

然后，将分好的数据集传入 train 函数进行模型训练，得到训练数据集和验证数据集的 RMSE，累加每一折的 RMSE，并输出当前折数、训练数据集 RMSE 和验证数据集 RMSE。最后，返回训练数据集的平均 RMSE 和验证数据集的...

The RMSE of RBF_SVR: 1.0447597239175044 The MAE of RBF_SVR: 0.6579144786196549 R^2 of RBF_SVR: 0.4542379453504922

这段代码输出了使用RBF_SVR模型预测的RMSE、MAE和R2等指标。RMSE是均方根误差，MAE是平均绝对误差，R2是决定系数。这些指标可以用来评估模型的性能。在这里，RMSE为1.0448，MAE为0.6579，R2为0.4542。可以看到，RMSE...

fold 0, train rmse: nan, valid rmse: nan fold 1, train rmse: nan, valid rmse: nan fold 2, train rmse: nan, valid rmse: nan fold 3, train rmse: nan, valid rmse: nan fold 4, train rmse: nan, valid rmse: nan 5-fold validation: avg train rmse: nan, avg valid rmse：nan 什么意思

"fold"代表交叉验证中的每一次验证，"train rmse"代表训练集的均方根误差，"valid rmse"代表验证集的均方根误差。这里的"nan"指的是"not a number"，也就是缺失值。因此，这段话的意思是这个机器学习模型在交叉验证...

# 在测试集上进行评估 with torch.no_grad(): user = torch.LongTensor(test_data['user_id'].values) item = torch.LongTensor(test_data['item_id'].values) rating = torch.FloatTensor(test_data['rating'].values) outputs = model(user, item) loss = criterion(outputs, rating) rmse = torch.sqrt(loss) print('RMSE on test set: %.3f ' % rmse.item())

这段代码是在测试集上使用模型进行预测，...test_data包含了测试集中的用户ID、物品ID和真实评分，通过调用模型的forward函数来预测评分，然后使用均方误差损失函数计算预测值与真实值的误差，最后计算RMSE并输出结果。

title = "The predicted values of test samples in LinearRegression\n" title += "RMSE = {:.2f}, estimator.coef_ = {:.2f}, {:.2f}, {:.2f}, {:.2f}, estimator.intercept_ = {:.2f}".format( rmse, estimator.coef_[0],estimator.coef_[1],estimator.coef_[2],estimator.coef_[3], estimator.intercept_[0])

rmse表示均方根误差，是一种线性回归模型的评估指标。如果您遇到了上述错误，可能是因为estimator.coef_或estimator.intercept_是标量变量，而您尝试使用索引访问它们。请检查您的代码，确保这些变量是标量...

import pandas as pdimport numpy as npimport matplotlib.pyplot as pltfrom statsmodels.tsa.arima.model import ARIMAfrom sklearn.metrics import mean_squared_error# 读取csv文件data = pd.read_csv("data.csv", header=None)# 划分训练集和测试集train_size = int(len(data) * 0.7)train_data, test_data = data[:train_size], data[train_size:]# 将时间编号设置为索引train_data.set_index(0, inplace=True)test_data.set_index(0, inplace=True)# 转换为时间序列train_ts = train_data[1]test_ts = test_data[1]# 训练ARIMA模型model = ARIMA(train_ts, order=(1, 1, 1))model_fit = model.fit()# 预测测试集数据predictions = model_fit.forecast(steps=len(test_ts))[0]# 计算rmsermse = np.sqrt(mean_squared_error(test_ts, predictions))print('Test RMSE: %.3f' % rmse)# 绘制图像plt.figure(figsize=(10, 6))plt.plot(train_ts, label='Train')plt.plot(test_ts, label='Test')plt.plot(test_ts.index, predictions, label='Predictions')plt.xlabel('Time/h')plt.ylabel('kwh')plt.title('ARIMA Model Predictions')plt.legend()plt.show()以上代码运行报错如下ValueError: 0 is not in range，请修正代码

print('Test RMSE: %.3f' % rmse) # 绘制图像 plt.figure(figsize=(10, 6)) plt.plot(train_ts, label='Train') plt.plot(test_ts, label='Test') plt.plot(test_ts.index, predictions, label='Predictions') plt....

MSE: 3730.56268950025 RMSE: 61.07833240601981 MAE: 61.02154649781597是否算预测失败

f(x) = aexp(bx) Coefficients (with 95% confidence bounds): a = 0.003231 (0.002873, 0.003589) b = 0.03553 (0.0351, 0.03595) Goodness of fit: SSE: 0.4229 R-square: 0.9998 Adjusted R-square: 0.9998 RMSE: 0.2056 可以解释一下我发的这个代码吗

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Test RMSE: 8.099

MSE: 3730.56268950025 RMSE: 61.07833240601981 MAE: 61.02154649781597是否算预测失败

f(x) = aexp(bx) Coefficients (with 95% confidence bounds): a = 0.003231 (0.002873, 0.003589) b = 0.03553 (0.0351, 0.03595) Goodness of fit: SSE: 0.4229 R-square: 0.9998 Adjusted R-square: 0.9998 RMSE: 0.2056 可以解释一下我发的这个代码吗

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RMSE_SPEC.m.zip_RMSE_RMSE.m文件_statistic _wave

Matlab程序用于求RMSE.m.zip

music_rmse_snr.m

Smoothing spline: f(x) = piecewise polynomial computed from p Smoothing parameter: p = 0.96732233 Goodness of fit: SSE: 0.01478 R-square: 0.998 Adjusted R-square: 0.9943 RMSE: 0.0695 解释下

Smoothing spline: f(x) = piecewise polynomial computed from p Smoothing parameter: p = 0.96732233 Goodness of fit: SSE: 0.01478 R-square: 0.998 Adjusted R-square: 0.9943 RMSE: 0.0695给出公式

f(x) = a*exp(b*x) Coefficients (with 95% confidence bounds): a = 0.003231 (0.002873, 0.003589) b = 0.03553 (0.0351, 0.03595) Goodness of fit: SSE: 0.4229 R-square: 0.9998 Adjusted R-square: 0.9998 RMSE: 0.2056 可以解释一下上述的代码吗

优化代码： plt.title(f"The predicted values of test samples in LinearRegression\nRMSE = {rmse:.2f}, " f"estimator.coef = {estimator.coef:.2f}%" f"estimator.intercept = {estimator.intercept:.2f}%")

title += "RMSE = {:.2f}, estimator.coef_ = {:.2f}%, {:.2f}%, {:.2f}%, {:.2f}%, estimator.intercept_ = {:.2f}%".format(rmse, estimator.coef_[0][1][2][3], estimator.intercept_) IndexError: invalid index to scalar variable.

title += "RMSE = {:.2f}, estimator.coef_ = {:.2f}%, estimator.intercept_ = {:.2f}%, {:.2f}%, {:.2f}%,{:.2f}%".format(rmse, estimator.coef_[0][1][2][3], estimator.intercept_) IndexError: invalid index to scalar variable.

The RMSE of RBF_SVR: 1.0447597239175044 The MAE of RBF_SVR: 0.6579144786196549 R^2 of RBF_SVR: 0.4542379453504922

fold 0, train rmse: nan, valid rmse: nan fold 1, train rmse: nan, valid rmse: nan fold 2, train rmse: nan, valid rmse: nan fold 3, train rmse: nan, valid rmse: nan fold 4, train rmse: nan, valid rmse: nan 5-fold validation: avg train rmse: nan, avg valid rmse：nan 什么意思

title = "The predicted values of test samples in LinearRegression\n" title += "RMSE = {:.2f}, estimator.coef_ = {:.2f}, {:.2f}, {:.2f}, {:.2f}, estimator.intercept_ = {:.2f}".format( rmse, estimator.coef_[0],estimator.coef_[1],estimator.coef_[2],estimator.coef_[3], estimator.intercept_[0])

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f(x) = aexp(bx) Coefficients (with 95% confidence bounds): a = 0.003231 (0.002873, 0.003589) b = 0.03553 (0.0351, 0.03595) Goodness of fit: SSE: 0.4229 R-square: 0.9998 Adjusted R-square: 0.9998 RMSE: 0.2056 可以解释一下上述的代码吗