from sklearn.neighbors import KNeighborsRegressor import pandas as pd KNN = KNeighborsRegressor(n_neighbors=7,weights='distance',p=1) KNN.fit(X_train, Y_train) Y_train_pred=KNN.predict(X_train) Y_test_pred = KNN.predict(X_test) print ("K近邻模型评估--训练集：") print ('r²:',KNN.score(X_train,Y_train)) print ('均方差',mean_squared_error(Y_train,Y_train_pred)) print ("K近邻模型评估--验证集：") print ('r²:',KNN.score(X_test,Y_test)) print ('均方差',mean_squared_error(Y_test,Y_test_pred))

这段代码使用了sklearn库中的KNeighborsRegressor类来实现K近邻回归模型，并使用distance作为权重函数，p=1表示使用曼哈顿距离。其中，X_train和Y_train是训练集的自变量和因变量，X_test和Y_test是验证集的自变量和因变量。在训练完成后，使用模型对训练集和验证集进行预测，并输出模型的评估指标，包括r²和均方差。其中，r²表示拟合程度的好坏，取值范围在0~1之间，越接近1表示拟合得越好；均方差则表示预测误差的大小，值越小表示预测误差越小。

相关问题

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.preprocessing import StandardScaler

这段代码主要是导入了一些常用的数据处理和机器学习库，包括pandas、sklearn.model_selection、sklearn.neighbors和sklearn.preprocessing。其中pandas库用于数据处理，sklearn.model_selection库用于模型选择和数据集划分，sklearn.neighbors库用于K近邻分类算法，sklearn.preprocessing库用于数据预 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
6 61 62 63 64 65 66 67 68 69 70 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
7 71 72 73 74 75 76 77 78 79 80 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
8 81 82 83 84 85 86 87 88 89 90 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
9 91 92 93 94 95 96 97 98 99 100 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100

这个结果就是将列名大于 50 小于 60 的部分删除后的新矩阵。

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

要在Python中运行这些代码，你需要确保已经安装了相应的包。大多数包都可以使用`pip`进行安装。

首先，在命令行中运行以下命令来安装所需的包：

pip install scikit-learn pandas matplotlib numpy

然后，在你的Python脚本中导入所需的库：

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.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score
import datetime
import multiprocessing as mp
from sklearn.ensemble import StackingClassifier
from sklearn.pipeline import make_pipeline
from sklearn.svm import LinearSVC

import random

请确保在运行这些代码之前，已经安装了所需的Python库。如果遇到任何问题，请确保已正确安装这些库，并且版本与代码兼容。