from sklearn.metrics import roc_curve,auc,confusion_matrix import pandas as pd import statsmodels.api as sm import matplotlib.pyplot as plt import numpy as np #库 w=pd.read_csv("D:/pythonProject/venv/Trans.csv") w['intercept']=1.0 x_c=w.columns[[4,0,1,2]];y_c=w.columns[3] X=w[x_c];y=w[y_c] X=np.array(X);y=np.array(y) zid=np.ones(len(y));zid[:int(len(y)*0.2)]=0 x_x=X[zid==1,:];y_x=y[zid==1] x_t=X[zid==0,:];y_t=y[zid==0] result=sm.Logit(y_x,x_x).fit print(result.summary)

import pandas as pd.docx

from sklearn.metrics import mean_squared_error X = wine_data.drop('alcohol', axis=1) y = wine_data['alcohol'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) ...

ANN.zip_sklearn_sklearn ANN_skleran ann_神经网络分类

sklearn_ann和skleran_ann可能是用户自定义的别名或者错误拼写，它们指的是sklearn中的神经网络API。在实际应用中，sklearn.neural_network.MLPClassifier是用于分类任务的主要类。以下是一些关于如何使用...

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 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...- 使用roc_curve和auc函数绘制ROC曲线； - 使用LabelEncoder和LabelBinarizer函数将标签进行编码。总的来说，这些功能可以帮助您构建和评估机器学习模型。

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.tree import DecisionTreeClassifier # 导入需要的模块 import warnings warnings.filterwarnings("ignore") from sklearn.metrics import confusion_matrix from itertools import cycle # from scipy import interp from sklearn.metrics import roc_curve, auc data = pd.read_csv('newdate.csv') print(data.head()) print(data.info()) print(data[data['Label'].isnull()]) data = data.dropna() print(data.info()) data['Label'] = data['Label'].map(int) print(data.info()) # 数据归一化 def normalization(data): _range = np.max(data) - np.min(data) return (data - np.min(data)) / _range data['铁水温度'] = normalization(data['铁水温度']) data['透气性指数'] = normalization(data['透气性指数']) print(data) # 相关性分析 plt.figure(figsize=(10, 10)) sns.heatmap(data=data.corr(), annot=True, cmap='Accent', vmax=1, vmin=-1) plt.show() df = pd.DataFrame(data.groupby(['Label'])['铁水温度'].count()) df.columns = ['num'] df.reset_index(inplace=True) print(df)解释每一行代码

from sklearn.metrics import roc_curve, auc data = pd.read_csv('newdate.csv') # 读取数据文件 print(data.head()) # 打印数据的前五行 print(data.info()) # 打印数据的信息（列名、数据类型、非空数量等） ...

from sklearn import metrics from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from imblearn.combine import SMOTETomek from sklearn.metrics import auc, roc_curve, roc_auc_score from sklearn.feature_selection import SelectFromModel import pandas as pd import numpy as np import matplotlib matplotlib.use('TkAgg') import matplotlib.pyplot as plt from sklearn.metrics import confusion_matrix #1、数据输入 df_table_all = pd.read_csv(r"D:\trainafter.csv",index_col=0) #2、目标和特征区分 X = df_table_all.drop(["Y"],axis=1).values Y = np.array(df_table_all["Y"]) #3、按比例切割数据 X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.3,random_state=0) #4、样本平衡， st= SMOTETomek() X_train_st,Y_train_st = st.fit_resample(X_train,Y_train) #4、特征选择： #创建特征选择模型 sfm = SelectFromModel(LogisticRegression(penalty='l1',C=1.0,solver="liblinear")) #训练特征选择模型 sfm.fit(X_train,Y_train) #讲数据转换，剩下重要的特征 X_train_tiny = sfm.transform(X_train) X_test_tiny = sfm.transform(X_test) #5、创建模型 model = LogisticRegression(penalty='l1',C=1.0,solver="liblinear") model.fit(X_train_st_tiny,Y_train_st) #6、预测 y_pred = model.predict_proba(X_test_st_tiny) y_cate = model.predict(X_test_st_tiny) c=confusion_matrix(Y_test,y_cate) print(c) def report_auc(y_true,y_prob,title,out_name="",lw=2): fpr,tpr,_=roc_curve(y_true,y_prob,pos_label=1) print(fpr) print(tpr) plt.figure() plt.plot(fpr,tpr,color="darkorange",lw=lw,lable="ROC curve") plt.plot([0,1],[0,1],color="yellow",lw=lw,linestyle="--") plt.xlim([0,1]) plt.ylim([0,1.05]) plt.title(title) plt.legend(loc='lower right') plt.show(0) plt.savefig(r"d:\LR"+out_name,dpi=800) plt.close("all") report_auc(Y_test,y_pred[:,1],"Logistic with L1 panetly",'LG')

SMOTETomek是用于处理样本不平衡问题的，auc、roc_curve、roc_auc_score是用于评估分类模型性能的指标，train_test_split是用于将数据集分为训练集和测试集的，SelectFromModel是用于特征选择的。同时，还使用了...

import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, roc_curve, roc_auc_score # 1. 数据读取与处理 data = pd.read_csv('data.csv') X = data.drop('target', axis=1) y = data['target'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 2. 模型训练 model = LogisticRegression() model.fit(X_train, y_train) # 3. 模型预测 y_pred = model.predict(X_test) y_prob = model.predict_proba(X_test)[:, 1] # 4. 绘制二分类混淆矩阵 confusion_mat = confusion_matrix(y_test, y_pred) plt.imshow(confusion_mat, cmap=plt.cm.Blues) plt.title('Confusion Matrix') plt.colorbar() tick_marks = np.arange(2) plt.xticks(tick_marks, ['0', '1']) plt.yticks(tick_marks, ['0', '1']) plt.xlabel('Predicted Label') plt.ylabel('True Label') for i in range(2): for j in range(2): plt.text(j, i, confusion_mat[i, j], ha='center', va='center', color='white' if confusion_mat[i, j] > confusion_mat.max() / 2 else 'black') plt.show() # 5. 计算精确率、召回率和F1-score precision = precision_score(y_test, y_pred) recall = recall_score(y_test, y_pred) f1 = f1_score(y_test, y_pred) # 6. 计算AUC指标和绘制ROC曲线 auc = roc_auc_score(y_test, y_prob) fpr, tpr, thresholds = roc_curve(y_test, y_prob) plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % auc) plt.plot([0, 1], [0, 1], 'k--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('ROC Curve') plt.legend(loc="lower right") plt.show() # 7. 输出结果 print('Precision:', precision) print('Recall:', recall) print('F1-score:', f1) print('AUC:', auc)对每行代码进行注释

from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, roc_curve, roc_auc_score - Import the necessary libraries. data = pd.read_csv('data.csv'...

import pandas as pd from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score, confusion_matrix,classification_report, roc_curve, auc import seaborn as sns import matplotlib.pyplot as plt # 读取数据 data = pd.read_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测样本.xlsx') # 分割训练集和验证集 train_data = data.sample(frac=0.8, random_state=1) test_data = data.drop(train_data.index) # 定义特征变量和目标变量 features = ['高程', '起伏度', '桥梁长', '道路长', '平均坡度', '平均地温', 'T小于0', '相态'] target = '交通风险' # 训练随机森林模型 rf = RandomForestClassifier(n_estimators=100, random_state=1) rf.fit(train_data[features], train_data[target]) # 在验证集上进行预测并计算精度、召回率和F1值等指标 pred = rf.predict(test_data[features]) accuracy = accuracy_score(test_data[target], pred) confusion_mat = confusion_matrix(test_data[target], pred) classification_rep = classification_report(test_data[target], pred) print('Accuracy:', accuracy) print('Confusion matrix:') print(confusion_mat) print('Classification report:') print(classification_rep) # 输出混淆矩阵图片 sns.heatmap(confusion_mat, annot=True, cmap="Blues") plt.show() # 计算并绘制ROC曲线和AUC值 fpr, tpr, thresholds = roc_curve(test_data[target], pred) roc_auc = auc(fpr, tpr) print('AUC:', roc_auc) plt.figure() lw = 2 plt.plot(fpr, tpr, color='darkorange', lw=lw, label='ROC curve (area = %0.2f)' % roc_auc) plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('Receiver operating characteristic') plt.legend(loc="lower right") plt.show() # 读取新数据文件并预测结果 new_data = pd.read_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测结果/交通风险预测096.xlsx') new_pred = rf.predict(new_data[features]) new_data['交通风险预测结果'] = new_pred new_data.to_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测结果/交通风险预测096结果.xlsx', index=False)改进代码使用多元roc曲线

from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, multiclass_roc_auc_score import seaborn as sns import matplotlib.pyplot as plt # 读取数据 data = pd.read_excel('E:...

import pandas as pd from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score, confusion_matrix,classification_report, roc_curve, auc import seaborn as sns import matplotlib.pyplot as plt # 读取数据 data = pd.read_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测样本.xlsx') # 分割训练集和验证集 train_data = data.sample(frac=0.8, random_state=1) test_data = data.drop(train_data.index) # 定义特征变量和目标变量 features = ['高程', '起伏度', '桥梁长', '道路长', '平均坡度', '平均地温', 'T小于0', '相态'] target = '交通风险' # 训练随机森林模型 rf = RandomForestClassifier(n_estimators=100, random_state=1) rf.fit(train_data[features], train_data[target]) # 在验证集上进行预测并计算精度、召回率和F1值等指标 pred = rf.predict(test_data[features]) accuracy = accuracy_score(test_data[target], pred) confusion_mat = confusion_matrix(test_data[target], pred) classification_rep = classification_report(test_data[target], pred) print('Accuracy:', accuracy) print('Confusion matrix:') print(confusion_mat) print('Classification report:') print(classification_rep) # 输出混淆矩阵图片 sns.heatmap(confusion_mat, annot=True, cmap="Blues") plt.show() # 读取新数据文件并预测结果 new_data = pd.read_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测结果/交通风险预测096.xlsx') new_pred = rf.predict(new_data[features]) new_data['交通风险预测结果'] = new_pred new_data.to_excel('E:/桌面/预测脆弱性/20230523/预测样本/预测结果/交通风险预测096结果.xlsx', index=False)输出混淆矩阵图片以及各分类精度

好的，你想让我输出混淆矩阵图片以及各分类精度...plt.show() # 输出各分类精度 print("各分类精度：") print(classification_rep) 你可以将这段代码添加到原有代码中，运行后即可输出混淆矩阵图片和各分类精度。

Demystifying the Confusion Matrix: How to Evaluate the Actual Performance of Classification Models

# Theoretical Foundation of Confusion Matrix ## Introduction and Definition The Confusion Matrix is a crucial tool in machine learning for evaluating the performance of classification models. It is a...

对pandas读取的数据进行逻辑回归评价预测，包括绘制二分类混淆矩阵、计算精确率与召回率、F1-score、ROC曲线与AUC指标代码

from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, roc_curve, roc_auc_score # 1. 数据读取与处理 data = pd.read_csv('data.csv') X = data.drop('...

如何用python代码实现支持向量机模型，具体的步骤如下1，对数据进行基本统计分析，对数据正确划分训练集、测试集。2，使用验证集对分类器调参，获取最优模型（对获得的最优模型，计算测试集上的Acc值、Precision值、Recall值、PR曲线、F1值、TPR值、FPR值、ROC曲线、AUC值。尝试对数据进行预处理

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve, auc, confusion_matrix import matplotlib.pyplot as plt # 读取数据集 data = pd.read_csv('...

请基于iris数据集，建立二分类逻辑回归模型，并绘制ROC曲线，计算AUC指标。

from sklearn.metrics import roc_curve, auc, confusion_matrix import matplotlib.pyplot as plt # 加载数据 iris = load_iris() X = iris.data[:, [2, 3]] # 选择花瓣长度和宽度作为特征 y = (iris.target == 0)...

利用Fisher判别分析法，写一份信用违约检测的python代码，并进行混淆矩阵、ROC、AUC曲线的评估检验

from sklearn.metrics import confusion_matrix, roc_curve, auc import matplotlib.pyplot as plt # 读取数据 data = pd.read_csv('credit.csv') # 数据预处理 X = data.iloc[:, :-1].values y = data.iloc[:, -1...

导入数据集，进行可视化分析，建立分类模型预测乳腺肿瘤良恶性。要求：输出模型AUC、准确度、精确度、召回率、F1-score等参数以及ROC曲线、混淆矩阵等图形。

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve, confusion_matrix import matplotlib.pyplot as plt # 预测和评估 predictions = model....

随机森林预测的python代码，多个特征变量，一个预测结果，输入是excel表，包括验证集和训练集比例，并对新的数据文件进行预测并输出预测结果excel表格以及精度、准确率、召回率和F1值、AUC曲线图、roc曲线图、和混淆矩阵热力图及评价结果解析

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_curve, auc, confusion_matrix import seaborn as sns import matplotlib.pyplot as plt # 读取数据 data = pd.read_...

对https://archive.ics.uci.edu/ml/datasets/Adult数据集构建一个分类器，测试分类器性能，列表展示分类器性能指标，画出ROC曲线的python代码

from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, roc_curve, auc from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier ...

电动车上牌管理系统 SSM毕业设计附带论文.zip

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