boston_xs=ss.fit_transform(boston_x)

I'm sorry, as a language model AI, I don't have the context of the code you provided. But generally, the code `ss.fit_transform(boston_x)` suggests that the `boston_x` data is being preprocessed using a `StandardScaler` object `ss`. The `fit_transform()` method is being used to first calculate the mean and standard deviation of the data and then transform the data into a standardized form where the mean is 0 and the standard deviation is 1. This is a common preprocessing step in machine learning to ensure that all features are on a similar scale and to prevent features with larger magnitudes from dominating the model.

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from sklearn.datasets import load_boston import numpy as np import pandas as pd data=pd.read_csv('./data_picture/chapter1/boston_house_prices.csv') data.head() from sklearn.model_selection import train_test_split X=data.drop('MEDV',axis=1) y=data['MEDV'] X=X.values y=y.values X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=33,test_size=0.25) from sklearn.preprocessing import StandardScaler ss_X=StandardScaler() scaler_X=ss_X.fit(X_train) X_train=scaler_X.transform(X_train) X_test=scaler_X.transform(X_test) from sklearn.neural_network import MLPRegressor model = MLPRegressor(solver='lbfgs', hidden_layer_sizes=(15,15), random_state=1) model.fit(X_train, y_train) from sklearn.metrics import r2_score,mean_squared_error,mean_absolute_error print('训练集回归评估指标：') model_score1=model.score(X_train,y_train) print('The accuracy of train data is',model_score1) print('测试集回归评估指标：') model_score2=model.score(X_test,y_test) print('The accuracy of test data is',model_score2) y_test_predict=model.predict(X_test) mse=mean_squared_error(y_test,y_test_predict) print('The value of mean_squared_error:',mse) mae=mean_absolute_error(y_test,y_test_predict) print('The value of mean_absolute_error:',mae)解析一下这段代码

这段代码是一个机器学习模型的训练和评估过程，具体步骤如下：

1. 从sklearn.datasets模块中导入boston数据集。
2. 使用pandas模块读取boston房价数据集，将其存储在data变量中，并显示前5行数据。
3. 从sklearn.model_selection模块中导入train_test_split函数，将数据集划分为训练集和测试集，其中训练集占75%，测试集占25%。
4. 从sklearn.preprocessing模块中导入StandardScaler类，用于对数据进行标准化处理。
5. 对训练集和测试集中的特征数据X进行标准化处理，使用fit()函数对scaler_X进行训练，再使用transform()函数对X_train和X_test进行标准化处理。
6. 从sklearn.neural_network模块中导入MLPRegressor类，用于创建多层感知机回归模型。
7. 创建MLPRegressor对象，并指定模型参数：solver='lbfgs', hidden_layer_sizes=(15, 15), random_state=1。其中，solver参数指定优化算法为拟牛顿法L-BFGS，hidden_layer_sizes参数指定模型中隐藏层的节点数为15，random_state参数指定随机种子。
8. 使用fit()函数对模型进行训练，输入训练集的特征数据X_train和目标数据y_train。
9. 从sklearn.metrics模块中导入r2_score、mean_squared_error和mean_absolute_error函数，用于评估模型的性能。
10. 分别使用score()函数计算训练集和测试集的R2得分，并输出结果。
11. 使用predict()函数对测试集进行预测，计算预测结果与实际结果之间的均方误差和平均绝对误差，并输出结果。

请调试这段代码以实现梯度上升模型进行波士顿房价预测，画出预测图，代码如下：import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error plt.style.use('ggplot') # 读入数据 data = pd.read_csv('D:\PycharmProjects\Boston_housing\housing.csv') X, y = data[data.columns.delete(-1)], data['MEDV'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=888) from sklearn.preprocessing import StandardScaler ss_x = StandardScaler() X_train = ss_x.fit_transform(X_train) X_test = ss_x.transform(X_test) ss_y = StandardScaler() y_train = ss_y.fit_transform(y_train.values.reshape(-1, 1)) y_test = ss_y.transform(y_test.values.reshape(-1, 1)) from sklearn import ensemble # params = {'n_estimators': 500, 'max_depth': 4, 'min_samples_split': 1,'learning_rate': 0.01, 'loss': 'ls'} # clf = ensemble.GradientBoostingRegressor(**params) clf = ensemble.GradientBoostingRegressor() clf.fit(X_train, y_train) clf_pre_values = clf.predict(X_test) # 预测值 print('SCORE:{:.4f}'.format(clf.score(X_test, y_test))) # 模型评分 print('RMSE:{:.4f}'.format(np.sqrt(mean_squared_error(y_test, clf_pre_values)))) # RMSE(标准误差) clf_pre = pd.DataFrame() clf_pre['Predict'] = clf_pre_values clf_pre['Truth'] = y_test.reset_index(drop=True) clf_pre.plot(figsize=(18, 8)) plt.figure() plt.show()

根据您提供的代码，我注意到您的代码中没有明确指定数据文件的路径。请确保`housing.csv`文件与您的代码在同一个文件夹中，并将文件的路径正确指定为相对路径或绝对路径。

另外，您的代码中没有导入`matplotlib.pyplot`模块。请在代码的开头添加以下导入语句：

import matplotlib.pyplot as plt

这样才能正确使用`plt.figure()`和`plt.show()`函数。

请尝试调试代码并确保数据文件路径正确，导入所需的模块，并重新运行代码。希望能帮到您！如果还有其他问题，请随时提问。