import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn import metrics #定义评估回归性能函数 def evaluation_metrics() : print("Mean Absolute Error:",metrics.mean_absolute_error(y_test,y_test_pred)) print("Mean Squared Error:",metrics.mean_squared_error(y_test,y_test_pred)) print("Root Mean Squared Error:",np.sqrt(metrics.mean_squared_error(y_test,y_test_pred))) print("r2_score : ",str(metrics.r2_score(y_test,y_test_pred))) #定义绘制Expermental-Predicted图像的函数 def graph(model) : plt.grid(True) plt.plot([1,12],[1,12],color='darkgrey',lw=3) plt.scatter(y_train,y_train_pred,color='c') plt.scatter(y_test,y_test_pred,color='salmon') plt.xlabel("Expermental Values",fontsize=14,fontweight='bold') plt.ylabel("Predicted Values",fontsize=14,fontweight='bold') plt.xlim(1,12) plt.ylim(1,12) plt.title(str(model),fontweight='bold') plt.savefig(str(model)+ "_test.tif",dpi=500) plt.show()

import pandas as pd import numpy as np import datetime import matplotlib.pyplot as plt from pandas.plotting import radviz该环境下输出每次更新的权重的代码

import numpy as np # 加载数据集 data = pd.read_csv('data.csv') labels = pd.read_csv('labels.csv') # 构建模型 model = keras.Sequential([ keras.layers.Dense(128, input_shape=(data.shape[1],), ...

优化这段代码# import modules 导入模块 from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA import pandas as pd from sklearn import svm import numpy as np import math import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from sklearn.model_selection import train_test_split from sklearn import datasets from matplotlib.colors import ListedColormap from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.model_selection import StratifiedShuffleSplit,StratifiedKFold from sklearn.model_selection import GridSearchCV from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict from sklearn.model_selection import KFold from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn import svm from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score from sklearn.metrics import roc_auc_score import math import datetime import multiprocessing as mp from sklearn.ensemble import StackingClassifier from sklearn.pipeline import make_pipeline from sklearn.svm import LinearSVC import random

import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from matplotlib.colors import ListedColormap from sklearn.model_selection import train_test_split, ...

import os,math from tensorflow.keras.layers import Dropout, Dense, SimpleRNN from sklearn.preprocessing import MinMaxScaler from sklearn import metrics import numpy as np import pandas as pd import tensorflow as tf import matplotlib.pyplot as plt 解释一下

- from sklearn import metrics：从sklearn库中导入了metrics模块，提供了一些评估指标的函数，用于评估模型的性能。 - import numpy as np：导入了numpy库，并将其重命名为np，用于进行数值计算和数组操作。 - ...

import numpy as np import matplotlib.pyplot as plt import pandas as pd import seaborn as sns from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn import metrics from sklearn import preprocessing

然后，你使用线性回归模型（LinearRegression）对数据进行拟合，并使用 metrics 库中的一些函数来评估模型的性能。最后，你使用 preprocessing 库对数据进行标准化处理。这些步骤都是非常常见的机器学习流程中的一...

import numpy as np import matplotlib.pyplot as plt import pandas as pd import math from keras.models import Sequential from keras.layers import Dense, Activation, Dropout, LSTM from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import mean_squared_error from keras import optimizers import time解说代码

- numpy 是 Python 中用于科学计算的一个库。 - matplotlib 是 Python 中一个常用的绘图库。 - pandas 是 Python 中用于数据分析的一个库。 - math 是 Python 中的数学库，提供了许多数学函数。 - keras ...

import pandas as pd import os from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score # 计算准确性 import numpy as np from sklearn.tree import plot_tree import matplotlib.pyplot as plt # 画图 from sklearn.metrics import confusion_matrix # 绘制混淆矩阵时使用 from sklearn.metrics import roc_curve, auc # 绘制ROC曲线时使用 from sklearn.preprocessing import label_binarize # 使用sklearn中的LabelBinarizer可以将多分类标签转化为二分类标签 from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import LabelBinarizer

您的代码看起来像是在使用Python中的一些流行的机器学习库，如Pandas、Scikit-learn和Numpy。通过这些库，您可以进行数据处理、训练和评估机器学习模型。具体来说，您的代码中使用了以下功能： - 导入了pandas、...

import numpy as np import pandas import pandas as pd import matplotlib from sklearn import naive_bayes from sklearn.preprocessing import MinMaxScaler from sklearn.preprocessing import StandardScaler from sklearn.preprocessing import normalize from sklearn.preprocessing import Binarizer from sklearn.impute import SimpleImputer from sklearn.preprocessing import OneHotEncoder import matplotlib.pyplot as plt from sklearn.metrics import roc_curve, auc from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import confusion_matrix matplotlib.rc("font", family='Microsoft YaHei') data=pd.read_csv(r'D:\杂货铺\机器学习\银行数据集.csv',header=None)

这段代码导入了一系列的Python库，包括NumPy、Pandas、Matplotlib、scikit-learn等。其中，NumPy是Python科学计算的核心库，Pandas是数据处理的重要库，Matplotlib是绘图库，scikit-learn是机器学习库。接下来，使用...

import pandas as pd import numpy as np import matplotlib.pyplot as plt from pylab import * import seaborn as sns import os from scipy import stats from sklearn import model_selection, preprocessing, naive_bayes, metrics, svm from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.linear_model import LogisticRegression from sklearn.neighbors import KNeighborsClassifier from sklearn import ensemble, tree # 忽略警告提示

- pylab：Matplotlib中的一个模块，提供了许多方便的函数和工具来创建各种类型的图表。 - seaborn：基于Matplotlib的数据可视化库，提供更高级的图表和更丰富的样式选项。 - os：Python的内置库，用于处理文件和目录...

import pandas as pd import numpy as np import scipy.stats as stats import seaborn as sns from sklearn.metrics import RocCurveDisplay from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix,accuracy_score from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn import tree from sklearn.decomposition import PCA import matplotlib.pyplot as plt from sklearn.tree import DecisionTreeClassifier请在此基础上续写代码块，要求是（1）读入数据后，选取自变量"sysBP", "diaBP","age","totChol","BMI", "heartRate", "glucose"记为X，因变量"TenYearCHD"记为y，组成新的数据集。¶

import matplotlib.pyplot as plt from sklearn.tree import DecisionTreeClassifier # 读入数据 data = pd.read_csv("heart.csv") # 选取自变量 X = data[["sysBP", "diaBP", "age", "totChol", "BMI", "heart...

写完这些代码后： import pandas as pd import numpy as np from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix,classification_report from sklearn.metrics import roc_curve import matplotlib.pyplot as plt from sklearn.linear_model import logisticRegression 出现了下面的错误 partially initialized module 'sklearn' has no attribute 'version' (most likely due to a circular import)请问这个错误怎么修改

这个错误通常是由于环境问题导致的。尝试升级 scikit-learn 库到最新版本，可以使用以下命令来升级： ...另外，确保在代码文件中没有命名为 "sklearn.py" 的文件，这可能会导致命名冲突并导致该错误。

解释这段代码import numpy as np import pandas as pd from datetime import datetime from scipy.stats import skew from scipy.special import boxcox1p from scipy.stats import boxcox_normmax from sklearn.linear_model import ElasticNetCV, LassoCV, RidgeCV, Ridge from sklearn.ensemble import GradientBoostingRegressor from sklearn.svm import SVR from sklearn.pipeline import make_pipeline from sklearn.preprocessing import RobustScaler, StandardScaler from sklearn.model_selection import KFold, cross_val_score from sklearn.metrics import mean_squared_error as mse from sklearn.metrics import make_scorer from sklearn.neighbors import LocalOutlierFactor from sklearn.linear_model import LinearRegression from mlxtend.regressor import StackingCVRegressor # from xgboost import XGBRegressor # from lightgbm import LGBMRegressor import matplotlib.pyplot as plt import seaborn as sns

这段代码是在Python中导入所需要的库和模块...- import matplotlib.pyplot as plt：导入matplotlib库，并将其简写为plt，用于绘制图形。 - import seaborn as sns：导入seaborn库，并将其简写为sns，用于绘制图形。

import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from tqdm.notebook import tqdm import math from sklearn.metrics import log_loss, make_scorer from sklearn.preprocessing import MinMaxScaler, StandardScaler from sklearn.model_selection import KFold, StratifiedKFold, StratifiedGroupKFold from lightgbm import LGBMClassifier import warnings warnings.filterwarnings("ignore")

对于您的代码导入部分，我注意到您导入了一些常用的数据处理和机器学习库，包括numpy、pandas、matplotlib、seaborn等。此外，还导入了一些特定的模块，如tqdm、math等。这些库和模块通常用于数据预处理、特征工程...

import numpy as np #矩阵操作函数库 import matplotlib.pyplot as plt #Matplotlib是Python的一个绘图库，是Python中最常用的可视化工具之一，可以非常方便地创建2D图表和一些基本的3D图表。 import pandas as pd #数据预处理库 import seaborn as sns # from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn import metrics from sklearn import preprocessing

8. sklearn.metrics：是Python的一个模型评估库，提供了许多常用的模型评估指标，如均方误差、准确率、召回率等。 9. sklearn.preprocessing：是Python的一个数据预处理库，提供了许多常用的数据预处理方法，如标准...

## 获取数据 from sklearn.metrics import r2_score import statsmodels.api as sm import numpy as np import pandas as pd import matplotlib.pyplot as plt data = pd.read_table('C:/Users/lb/Desktop/test/k-means_data.txt',sep='\t',engine="python",encoding = 'gbk') data.columns.values data.head()改为读取csv文件

可以使用pd.read_csv()函数读取csv...import matplotlib.pyplot as plt data = pd.read_csv('C:/Users/lb/Desktop/test/k-means_data.txt', delimiter=',', encoding='gbk') data.columns.values data.head()

import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn import metrics import matplotlib.pyplot as plt

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