import pandas as pd from sklearn.neighbors import KNeighborsClassifier from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split from sklearn.metrics import f1_score # 读取训练集和测试集数据 data_hk = pd.read_csv("员工满意度_train.csv", engine='python') # 填充缺失值 data_hk.fillna(0, inplace=True) data_hk = data_hk.drop(labels=['division'],axis=1) # 将分类特征进行编码 encoder = LabelEncoder() data_hk['package'] = encoder.fit_transform(data_hk['package'].astype(str)) # 划分训练集和验证集 X_train, X_test, y_train, y_test = train_test_split(data_hk.iloc[:, 0:-1], data_hk.iloc[:, -1], test_size=0.45, random_state=10) # 模型训练 model = KNeighborsClassifier(n_neighbors=4) model.fit(X_train, y_train) # 读取测试集数据 forecast_hk = pd.read_csv("员工满意度_test_nolabel.csv", engine='python') forecast_hk = forecast_hk.drop(labels=['division'],axis=1) forecast_hk.fillna(0, inplace=True) forecast_hk['package'] = encoder.transform(forecast_hk['package'].astype(str)) # 在测试集上进行预测 y_predict = model.predict(forecast_hk) # 将预测结果添加到测试集中 forecast_hk['salary'] = y_predict forecast_hk.to_csv("员工满意度_test_nolabel.csv", index=False) # 在验证集上进行预测和评估 y_pred_test = model.predict(X_test) score = f1_score(y_test, y_pred_test, average='macro') print("模型评价（f1-score）：", score)

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...

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....

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...

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...

请给我修改后的这份代码，使它的模型评价高于0.6 import pandas as pd from sklearn.neighbors import KNeighborsClassifier from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split from sklearn.metrics import f1_score 读取训练集和测试集数据 data_hk = pd.read_csv("员工满意度_train.csv", engine='python') 填充缺失值 data_hk.fillna(0, inplace=True) data_hk = data_hk.drop(labels=['division'],axis=1) 将分类特征进行编码 encoder = LabelEncoder() data_hk['package'] = encoder.fit_transform(data_hk['package'].astype(str)) 划分训练集和验证集 X_train, X_test, y_train, y_test = train_test_split(data_hk.iloc[:, 0:-1], data_hk.iloc[:, -1], test_size=0.45, random_state=10) 模型训练 model = KNeighborsClassifier(n_neighbors=4) model.fit(X_train, y_train) 读取测试集数据 forecast_hk = pd.read_csv("员工满意度_test_nolabel.csv", engine='python') forecast_hk = forecast_hk.drop(labels=['division'],axis=1) forecast_hk.fillna(0, inplace=True) forecast_hk['package'] = encoder.transform(forecast_hk['package'].astype(str)) 在测试集上进行预测 y_predict = model.predict(forecast_hk) 将预测结果添加到测试集中 forecast_hk['salary'] = y_predict forecast_hk.to_csv("员工满意度_test_nolabel.csv", index=False) 在验证集上进行预测和评估 y_pred_test = model.predict(X_test) score = f1_score(y_test, y_pred_test, average='macro') print("模型评价（f1-score）：", score)

from sklearn.model_selection import train_test_split from sklearn.metrics import f1_score # 读取训练集和测试集数据 data_hk = pd.read_csv("员工满意度_train.csv", engine='python') # 填充缺失值 data_hk...

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 as pd from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score from sklearn.metrics import confusion_matrix import matplotlib.pyplot as plt from termcolor import colored as cl import itertools from sklearn.preprocessing import StandardScaler from sklearn.tree import DecisionTreeClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC from sklearn.ensemble import RandomForestClassifier from xgboost import XGBClassifier from sklearn.neural_network import MLPClassifier from sklearn.ensemble import VotingClassifier # 定义模型评估函数 def evaluate_model(y_true, y_pred): accuracy = accuracy_score(y_true, y_pred) precision = precision_score(y_true, y_pred, pos_label='Good') recall = recall_score(y_true, y_pred, pos_label='Good') f1 = f1_score(y_true, y_pred, pos_label='Good') print("准确率:", accuracy) print("精确率:", precision) print("召回率:", recall) print("F1 分数:", f1) # 读取数据集 data = pd.read_csv('F:\数据\大学\专业课\模式识别\大作业\数据集1\data clean Terklasifikasi baru 22 juli 2015 all.csv', skiprows=16, header=None) # 检查数据集 print(data.head()) # 划分特征向量和标签 X = data.iloc[:, :-1] y = data.iloc[:, -1] # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 6. XGBoost xgb = XGBClassifier(max_depth=4) y_test = np.array(y_test, dtype=int) xgb.fit(X_train, y_train) xgb_pred = xgb.predict(X_test) print("\nXGBoost评估结果：") evaluate_model(y_test, xgb_pred)

5. 划分训练集和测试集：使用sklearn库的train_test_split()函数将数据集划分为训练集和测试集。 6. 使用XGBoost算法进行分类：使用XGBClassifier类构建XGBoost分类器，并使用fit()函数将训练集拟合到该分类器中。...

iris = datasets.load_iris() from skLearn. preprocessing import MinMaxScaLer iris_ data=MinMaxScaLer().fit transform(iris .data) print(iris_ data[0:5,:l) iris_ df=pd. DataFrame(iris_ data scolumns=['Sepal Length', 'Sepal Width's 'Petal Length', 'Peta iris_ df['target' ]=iris. target from skLearn.model seLection import train. _test _split X_train, X_test, y_train, y_test = train_test_ split(iris. _df.iloc[:0:4], iris. df['target'], random. state=. 14) from skLearn . neighbors import KNeighborsClassifier knn = KNeighborsCLassifier() knn.fit(X_ train, y_ train) y_ predicted = knn. predict(X_test) accuracy = np.mean(y predicted == y_ test) *100 print('当前分类评估器是: knn ') print('当前Accuracy是: %.1f' %accuracy + '%' )

4. 使用train_test_split函数将数据集分为训练集和测试集。 5. 导入KNeighborsClassifier函数，并创建一个KNN分类器。 6. 使用训练集数据拟合KNN分类器，然后使用测试集数据对其进行预测。 7. 计算预测结果与...

import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier df = pd.read_excel('C:/Users/Lenovo、、、、王煜欣/Desktop/newas.xlsx') df.head() print(df.head()) df['Gender'].replace(to_replace={'Female':0,'Male':1},inplace=True) df['Geography'].replace(to_replace={'France':0,'Spain':2,'Germany':1},inplace=True) df['Card Type'].replace(to_replace={'青铜':0,'白银':1,'黄金':2,'钻石':3},inplace=True) X = df[['AUM_before','AUM_now','rate','CreditScore','Gender','Age','Tenure','Balance','NumOfProducts','HasCrCard']].values y = df['existed'].values X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42) knn = KNeighborsClassifier(n_neighbors=5) knn.fit(X_train,y_train) print('knn模型准确率：', knn.score(X_test,y_test)) #逻辑回归 from sklearn.linear_model import LogisticRegression from sklearn.preprocessing import StandardScaler import features as feature lr = LogisticRegression() features_encoded = pd.get_dummies(feature) scaler = StandardScaler() features_temp = scaler.fit_transform(features_encoded) lr.fit(X_train, y_train) print('逻辑回归模型准确率：', lr.score(X_test, y_test)) #shap import shap as sh explainer = sh.Explainer(model=lr) shap_values = explainer(X_train) sh.plots.force(shap_values[0])

接下来，将特征和标签分别赋值给变量X和y，并使用train_test_split函数将数据集划分为训练集和测试集。然后，使用KNeighborsClassifier算法构建了一个K近邻分类器模型，并使用训练集进行训练，最后打印出模型在...

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用python结合tkinter库实现def standard_X(X): X_copy = X.copy() # 拿数据 for col_name in X_copy.columns: # 取列名 col_data = X_copy[[col_name]] # 根据列名拿列数据，两个方括号是因为要二维数组 # fit_transform stand_data = StandardScaler().fit_transform(col_data.values) # 标准化 X_copy[col_name] = stand_data # 将数据替换成标准化后的数据 return X_copy standard_X(X).describe([0.01,0.25,0.5,0.75,0.99]).T stand_X = standard_X(X) for col_name in stand_X.columns: sns.distplot(stand_X[col_name]) plt.title(col_name) plt.show() X[0] = pd.cut(X[0],bins = 5,labels = [0,1,2,3,4]) sns.countplot(X[0]) for col_name in X.columns: X[col_name] = pd.cut(X[col_name],bins = 5,label knn = KNeighborsClassifier() X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2,random_state = 1) knn.fit(X_train,y_train) KNeighborsClassifier() knn.score(X_train,y_train) knn.score(X_test,y_test)成一个界面

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