import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error import numpy as np # 数据输入：手动创建数据集，实际应用中可以从CSV或Excel文件加载数据 data = { "日期": ["2025/4/1"] * 24, "时间": [f"{i}:00:00" for i in range(24)], "气温℃": [3.58, 3.70, 3.22, 2.23, 1.94, 2.42, 2.23, 2.87, 3.31, 10.53, 11.32, 11.63, 14.11, 14.18, 14.08, 14.69, 14.66, 11.51, 11.39, 12.41, 11.57, 7.35, 5.35], "湿度%": [11.26, 13.23, 14.41, 18.66, 17.96, 21.34, 21.91, 20.31, 19.76, 10.60, 9.40, 9.15, 6.93, 6.34, 6.19, 5.95, 6.04, 7.34, 5.95, 4.26, 4.57, 9.14, 9.58], "气压hPa": [867.53, 867.39, 867.19, 866.97, 866.44, 866.28, 867.03, 867.62, 867.86, 868.11, 868.08, 868.01, 867.15, 866.47, 865.33, 865.03, 864.78, 865.46, 864.93, 865.26, 866.01, 866.25, 866.10], "风向°": [275.53, 276.13, 279.62, 281.28, 284.63, 296.73, 294.89, 298.60, 300.94, 304.77, 312.40, 318.26, 318.40, 324.85, 330.32, 334.99, 335.53, 340.04, 5.06, 48.04, 74.37, 105.08, 106.24], "输出功率(MW)": [0.31, 0.48, 0.49, 0.47, 0.43, 0.42, 0.34, 0.28, 0.18, 0.11, 0.17, 0.10, 0.06, 0.05, 0.04, 0.04, 0.02, 0, 0, 0.0037, 0.013, 0.030, 0.017], "风速(m/s)": [6.92, 7.99, 8.06, 7.96, 7.66, 7.61, 7.12, 6.68, 5.81, 5.11, 5.73, 4.88, 4.28, 3.98, 3.81, 3.68, 3.20, 1.88, 1.39, 2.39, 3.09, 3.51, 3.51] } # 转换为DataFrame df = pd.DataFrame(data) # 选择预测目标：假设我们想预测“输出功率(MW)”基于其他变量 X = df[["气温℃", "湿度%", "气压hPa", "风向°", "风速(m/s)"]] # 特征 y = df["输出功率(MW)"] # 目标变量 # 拆分训练数据和测试数据 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 创建并训练线性回归模型 model = LinearRegression() model.fit(X_train, y_train) # 在测试集上预测 y_pred = model.predict(X_test) # 评估模型 mse = mean_squared_error(y_test, y_pred) print(f"均方误差(MSE): {mse}") # 输出预测结果 print(f"预测值: {y_pred}") print(f"实际值: {y_test.values}") 函数报错 File “C:\Users\22564\PycharmProjects\pythonProject26.venv\Lib\site-packages\pandas\core\internals\construction.py”, line 677, in _extract_index raise ValueError(“All arrays must be of the same length”) ValueError: All arrays must be of the same length

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.metrics import r2_score df = pd.read_csv('train_price.csv') # 筛选bodyType为'微型车'的样本 df = df[df['bodyType'] == '微型车'] # 选择price作为目标变量,yearMade、modelId作为特征变量 X = df[['yearMade', 'modelId']] y = df['price'] # 使用train_test_split划分,random_state为学号后4位 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234) # 训练线性回归模型 lr = LinearRegression() lr.fit(X_train, y_train) # 预测测试样本并计算R2 y_pred = lr.predict(X_test) r2 = r2_score(y_test, y_pred) print('R2值为:', r2)

其中使用了pandas库读取CSV文件，使用train_test_split函数将数据集划分为训练集和测试集，使用LinearRegression类创建一个线性回归模型，并使用fit方法对训练数据进行拟合，使用predict方法对测试数据进行预测，并...

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 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

- from sklearn.model_selection import train_test_split 导入了 scikit-learn 库中的 train_test_split 函数，用于将数据集拆分为训练集和测试集。 - from sklearn.linear_model import LogisticRegression ...

在Python中想运行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怎么操作

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 ...

运行下面代码需要安装哪些包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

- pandas：提供了高性能、易于使用的数据结构和数据分析工具。可以使用pip install pandas进行安装。 - matplotlib：用于绘制图表和数据可视化的库。可以使用pip install matplotlib进行安装。这些包是常用的...

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

from sklearn.model_selection import train_test_split, StratifiedShuffleSplit, StratifiedKFold, GridSearchCV, LeaveOneOut, cross_val_predict, KFold from sklearn.decomposition import PCA from sklearn....

import pandas as pd import numpy as np import winreg import sklearn from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split from sklearn.linear_model import Ridge###导入岭回归算法 from sklearn.metrics import r2_score import winreg

你已经成功导入了所需的库：pandas、numpy、winreg、sklearn以及sklearn中的LinearRegression、train_test_split、Ridge和r2_score。现在你可以使用这些库中提供的功能来进行数据处理、建模和评估等任务。如果你...

import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.metrics import classification_report from sklearn.metrics import confusion_matrix

这段代码是在 Python 中导入了一些常用的机器学习库和模块，包括 pandas、numpy、matplotlib、sklearn 等。其中： - pandas 是 Python 中常用的数据分析库，可以用来读取和处理数据； - numpy 是 Python 中常用的...

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

- pandas：用于数据处理和分析的Python库。 - numpy：用于数值计算的Python库。 - matplotlib：用于绘制数据可视化图表的Python库。 - 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...

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...

# 数据集特征分析相关库import pandas as pdimport matplotlib.pyplot as pltimport seaborn as sns# 数据集预处理相关库from sklearn.preprocessing import StandardScalerfrom sklearn.model_selection import train_test_split# K近邻算法相关库from sklearn.neighbors import KNeighborsClassifierfrom sklearn.model_selection import cross_val_scorefrom sklearn.metrics import precision_score,recall_score,f1_scorefrom sklearn.metrics import precision_recall_curve,roc_curve,average_precision_score,auc# 决策树相关库from sklearn.tree import DecisionTreeClassifier# 随机森林相关库from sklearn.ensemble import RandomForestClassifier# 逻辑回归相关库from sklearn.linear_model import LogisticRegression# SGD分类相关库from sklearn.linear_model import SGDClassifier

from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler X = data.drop(columns=['target']) y = data['target'] # 划分训练集和测试集 X_train, X_test, y_...

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。

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。¶

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 ...

#本章需导入的模块 import numpy as np import pandas as pd import matplotlib.pyplot as plt %matplotlib inline plt.rcParams['font.sans-serif']=['SimHei'] #解决中文显示乱码问题 plt.rcParams['axes.unicode_minus']=False import warnings warnings.filterwarnings(action = 'ignore') from sklearn.metrics import confusion_matrix,f1_score,roc_curve, auc, precision_recall_curve,accuracy_score from sklearn.model_selection import train_test_split,KFold,LeaveOneOut,LeavePOut # 数据集划分方法 from sklearn.model_selection import cross_val_score,cross_validate # 计算交叉验证下的测试误差 from sklearn import preprocessing import sklearn.linear_model as LM from sklearn import neighbors

from sklearn.linear_model import LinearRegression import numpy as np X = np.array([[1], [2], [3]]) Y = np.array([2, 4, 6]) model = LinearRegression().fit(X, Y) predictions = model.predict(np.array(...

import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error, r2_score import matplotlib.pyplot as plt data = { 'Population': [5, 10, 15, 20, 25], 'Profit' : [10, 20, 25, 30, 40] } df = pd.DataFrame(data) X = df[['Population']].values.reshape(-1,1) # 自变量 X (城市人口) y = df['Profit'].values # 因变量 y (利润) # 划分训练集/测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) model = LinearRegression() model.fit(X_train, y_train) predictions = model.predict(X_test) print(f"Intercept: {model.intercept_}") # 输出截距项 print(f"Coefficients: {model.coef_[0]}") # 输出斜率系数 new_population = [[7], [8]] # 新的城市人口数组 predicted_profits = model.predict(new_population) for pop, profit in zip(new_population, predicted_profits): print(f"For population={pop}, the estimated profit is ${profit*1e3:.2f}") 第1/5条运行结果运行结果错误,该组未通过第2/5条运行结果运行结果错误,该组未通过第3/5条运行结果运行结果错误,该组未通过第4/5条运行结果运行结果错误,该组未通过第5/5条运行结果运行结果错误,该组未通过

from sklearn.model_selection import train_test_split # 假设data为DataFrame，目标列为'y' X = data[['feature_column']] # 注意双括号确保二维结构 y = data['y'] X_train, X_test, y_train, y_test = train_...

import pandas as pd import numpy as np from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics import f1_score from sklearn.svm import SVC from sklearn.model_selection import KFold from sklearn import tree from sklearn.naive_bayes import GaussianNB from sklearn.linear_model import LogisticRegression from sklearn.neighbors import KNeighborsClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import BaggingClassifier from sklearn.preprocessing import Normalizer if name == "main": ''' 1.数据处理部分 ''' train_data = pd.read_csv("train.txt", header = None, sep = ",") test_data = pd.read_csv("test.txt", header = None, sep = ",") train_data.dropna(inplace = True) #删除有缺失值的行 test_data.dropna(inplace = True) ''' 2.文本-向量转换处理，原理参见tf-idf的应用 - 可以自行实现word2vec等更多方法 ''' x_train = list(train_data[0]) y_train = train_data[1] x_test = list(test_data[0]) y_test = test_data[1]

from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X_tfidf, data['label'], test_size=0.2, random_state=42) svc_model = SVC(kernel='linear', C=...

import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from sklearn.linear_model import LinearRegression #1.加载数据 data = pd.read_csv(r"E:\winequality-red.csv") data = np.array(data) x = data[:,0:11] y = data[:,-1] #2.划分训练集和测试集 train_x,test_x,train_y,test_y=train_test_split(x,y,test_size=0.3) #3.构建分类模型 model = LinearRegression() #4.训练模型 model.fit(train_x,train_y) #5.预测 test_y_pre = model.predict(test_x) #6.评估模型 print(accuracy_score(test_y_pre,test_y))

2.使用 train_test_split 方法将数据集分为训练集和测试集，其中 test_size=0.3 表示将 30% 的数据用于测试。 3.使用 LinearRegression 构建线性回归模型。 4.使用训练集数据对模型进行训练，即调用 fit ...

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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

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import pandas as pd import numpy as np import winreg import sklearn from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split from sklearn.linear_model import Ridge###导入岭回归算法 from sklearn.metrics import r2_score import winreg

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