import pandas as pd from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score, confusion_matrix data = pd.read_csv('data.csv') # 将分类变量（department、status和age）转换为数字 data['department'] = pd.factorize(data['department'])[0] data['status'] = pd.factorize(data['status'])[0] data['age'] = pd.factorize(data['age'])[0] salary_map = {'26K...30K': 1,'31K...35K': 2,'36K...40K': 3,'41K...45K': 4,'46K...50K': 5,'66K...70K': 6} data['salary'] = data['salary'].map(salary_map) X = data[['department', 'age', 'salary', 'count']]# 准备数据 X.columns = ['Department', 'Age', 'Salary', 'Count']# 为每一列指定特征名称 y = data['status'] # 将数据拆分为训练和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) model = DecisionTreeClassifier()# 初始化决策树分类器模型 model.fit(X_train, y_train)# 训练模型 y_pred = model.predict(X_test)# 模型评估 acc_score = accuracy_score(y_test, y_pred) new_data = [[1, 1, 5, 10]]# 预测新数据 new_data_df = pd.DataFrame(data=new_data, columns=['Department', 'Age', 'Salary', 'Count']) new_status = pd.factorize(data['status'])[1][int(model.predict(new_data_df)[0])] if new_status==0: print('预测值为:senior') elif new_status==1: print('预测值为:junior') print(data) 这段代码详细解释一下

pd_split_train_test.rar_pandas_pandas对数据分类_pd.split_split_数据分类

Python库 | sklearn_cv_pandas-0.0.4.tar.gz

资源分类：Python库所属语言：Python 资源全名：sklearn_cv_pandas-0.0.4.tar.gz 资源来源：官方安装方法：https://lanzao.blog.csdn.net/article/details/101784059

详解pandas库pd.read_excel操作读取excel文件参数整理与实例

主要介绍了pandas库pd.read_excel操作读取excel文件参数整理与实例，小编觉得挺不错的，现在分享给大家，也给大家做个参考。一起跟随小编过来看看吧

import pandas as pd from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score import numpy as np data = pd.read_csv('iris.csv', header=None, names=['sepald

3. 使用train_test_split函数将数据集划分为训练集和测试集，其中测试集占总数据集的50%。 4. 创建DecisionTreeClassifier类的实例，并将其存储在clf变量中。 5. 使用fit方法在训练集上训练决策树模型。 6. 使用...

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 ...

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

- 使用sklearn中的train_test_split函数将数据集分为训练集和测试集； - 使用RandomForestClassifier（随机森林分类器）进行分类任务； - 使用accuracy_score函数计算模型的准确度； - 导入numpy库； - 使用plot_...

# 导入必要的库 import pandas as pd from decision_tree_classifier import DecisionTreeClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score # 读取并展示数据集 data = pd.read_csv("heart.csv") print(data.head()) # 数据处理和特征选择 X = data.drop(['target'], axis=1) y = data['target'] # 将数据分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 使用 DecisionTreeClassifier() 函数构建决策树模型 model = DecisionTreeClassifier() model.fit(X_train, y_train) # 在测试集上进行预测 y_pred = model.predict(X_test) # 打印模型精度得分 accuracy = accuracy_score(y_test, y_pred) print('Accuracy:', accuracy) 帮我写一份与我这个代码相适应的决策树算法

from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score # 读取并展示数据集 data = pd.read_csv("heart.csv") print(data.head()) # 数据处理和特征选择 X = data....

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score # 读入数据集 data = pd.read_csv('kbfz.csv') # 打印数据集的形状和前5行的内容 print(data.shape) print(data.head()) # 提取特征和目标变量 X = data[['Thought-tendency', 'gender', 'Special-behavior-trajectory', 'Tobacco-alcohol']] y = data['Terrorism'] # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0) # 建立决策树模型，模型参数不需设置 dtc = DecisionTreeClassifier() # 训练模型 dtc.fit(X_train, y_train) # 预测测试集结果 y_pred = dtc.predict(X_test) # 计算分类准确率 accuracy = accuracy_score(y_test, y_pred) # 打印分类准确率 print("The accuracy is :" accuracy)

from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score # 读入数据集 data = pd.read_csv('kbfz.csv') # 打印数据...

import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score # 加载数据 data = pd.read_csv('heart_2020_cleaned.csv') # 特征工程 X = data.drop('HeartDisease', axis=1) y = data['HeartDisease'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) # 模型训练 model = DecisionTreeClassifier(max_depth=3) model.fit(X_train, y_train) # 模型评估 y_pred = model.predict(X_test) acc = accuracy_score(y_test, y_pred) precision = precision_score(y_test, y_pred) recall = recall_score(y_test, y_pred) f1 = f1_score(y_test, y_pred) print('Accuracy:', acc) print('Precision:', precision) print('Recall:', recall) print('F1:', f1)转换数值类型

data['SomeFeature'] = data['SomeFeature'].astype(float) 其中，SomeFeature表示需要转换数据类型的特征名，float表示需要转换为的数据类型。可以根据实际情况选择需要转换为的数据类型。

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。...

简述以下代码的思路：import logging import pandas as pd from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from sklearn.tree import DecisionTreeClassifier logging.basicConfig(level=logging.INFO) trainData = pd.read_csv('data_train.txt', header=None, sep=' ') testData = pd.read_csv('data_test.txt', header=None, sep=' ') logging.info("读取完毕.") features = list(range(54)) X_train , X_val , Y_train , Y_val = train_test_split(trainData[features],trainData[54],test_size=0.2,random_state=42) logging.info("数据拆分完毕.") model = DecisionTreeClassifier(criterion="entropy", max_features=1.0, random_state=114514) model.fit(X_train , Y_train) logging.info("数据训练完毕.") pred = model.predict(X_val) acc = accuracy_score(Y_val , pred) logging.info(f"预测精确度为:{acc:.4f}") logging.info("验证完毕.") testResult = model.predict(testData[features]) pd.DataFrame(testResult).to_csv("model.txt",index=False,header=None) logging.info("计算完毕 ,结果已输出到model.txt文件中.")

1.导入所需的库，包括logging、pandas、sklearn.model_selection、sklearn.metrics和sklearn.tree。 2.使用pandas读取训练集和测试集的数据，并将读取信息输出到日志中。 3.定义特征列表，将训练集按照80%和20%的...

import pandas as pd from sklearn.datasets import load_wine from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.feature_selection import SelectKBest, f_classif from sklearn.decomposition import PCA from sklearn.metrics import accuracy_score, classification_report from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score from sklearn.ensemble import RandomForestClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.naive_bayes import GaussianNB from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC data = load_wine() # 导入数据集 X = pd.DataFrame(data.data, columns=data.feature_names) y = pd.Series(data.target) # 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=0) # 构建分类模型 model = LogisticRegression() model.fit(X_train, y_train) # 预测测试集结果 y_pred = model.predict(X_test) #评估模型性能 accuracy = accuracy_score(y_test, y_pred) report = classification_report(y_test, y_pred) print('准确率:', accuracy) # 特征选择 selector = SelectKBest(f_classif, k=6) X_new = selector.fit_transform(X, y) print('所选特征:', selector.get_support()) # 模型降维 pca = PCA(n_components=2) X_new = pca.fit_transform(X_new) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X_new, y, test_size=0.2, random_state=0) def Sf(model,X_train, X_test, y_train, y_test,modelname): mode = model() mode.fit(X_train, y_train) y_pred = mode.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print(modelname, accuracy) importance = mode.feature_importances_ print(importance) def Sf1(model,X_train, X_test, y_train, y_test,modelname): mode = model() mode.fit(X_train, y_train) y_pred = mode.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print(modelname, accuracy) modelname='支持向量机' Sf1(SVC,X_train, X_test, y_train, y_test,modelname) modelname='逻辑回归' Sf1(LogisticRegression,X_train, X_test, y_train, y_test,modelname) modelname='高斯朴素贝叶斯算法训练分类器' Sf1(GaussianNB,X_train, X_test, y_train, y_test,modelname) modelname='K近邻分类' Sf1(KNeighborsClassifier,X_train, X_test, y_train, y_test,modelname) modelname='决策树分类' Sf(DecisionTreeClassifier,X_train, X_test, y_train, y_test,modelname) modelname='随机森林分类' Sf(RandomForestClassifier,X_train, X_test, y_train, y_test,modelname)加一个画图展示

import matplotlib.pyplot as plt importance = mode.feature_importances_ features = list(X.columns) plt.barh(features, importance) plt.title('Feature Importance') plt.xlabel('Importance') plt.show() ...

# Importing the dataset dataset = pd.read_csv('Iris.csv') X = dataset.iloc[:, :-1].values y = dataset.iloc[:, -1].values # Splitting the dataset into the Training set and Test set from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0) # Feature Scaling from sklearn.preprocessing import StandardScaler sc = StandardScaler() X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test) # Training the Decision Tree Classification model on the Training set from sklearn.tree import DecisionTreeClassifier classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0) classifier.fit(X_train, y_train) # Making the Confusion Matrix from sklearn.metrics import confusion_matrix, accuracy_score y_pred = classifier.predict(X_test) cm = confusion_matrix(y_test, y_pred) print(cm) print(accuracy_score(y_test, y_pred))解释每行代码

from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0) 这一部分代码使用了train_test_split函数将数据集...

import pandas as pd data = pd.read_csv('heart.csv') # 目标值和特征值 x = data.drop(['HeartDisease'], axis=1) y = data['HeartDisease'] # 导入库 from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler # 划分数据集 x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=20) # 特征预处理 sc = StandardScaler() # 标准化 x_train = sc.fit_transform(x_train) x_test = sc.transform(x_test) # 导入相关库 from sklearn.metrics import accuracy_score from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report classifier = DecisionTreeClassifier(criterion="gini") # CART算法 classifier.fit(x_train, y_train.ravel()) y_pred1 = classifier.predict(x_test) # 计算准确率 score1 = classifier.score(x_test, y_test) print("准确率为：\n", score1) # 查看精确率、召回率、F1-score report1 = classification_report(y_test, y_pred1, labels=[0, 1], target_names=['Not sick', 'sick']) print(report1)请指出这段机器学习模型中的问题

2. 模型评估不全面：虽然计算了模型的准确率（accuracy），但没有对其他重要的评估指标进行分析，比如精确率（precision）、召回率（recall）和 F1-score。这些指标对于了解模型的性能和预测结果的可靠性非常重要。 ...

import pandas as pd from sklearn.model_selection import train_test_split # 导入数据并添加列名 columns = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'Class_Values'] car_data = pd.read_csv('car.data', header=None, names=columns) # 将 Class Values 转换为数字 class_map = {'unacc': 0, 'acc': 1, 'good': 2, 'vgood': 3} car_data['Class_Values'] = car_data['Class_Values'].map(class_map) # 划分训练集和测试集 train_data, test_data = train_test_split(car_data, test_size=0.2, random_state=42) from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score # 定义特征变量和目标变量 feature_cols = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety'] target_col = 'Class_Values' # 建立决策树模型 dt = DecisionTreeClassifier(max_depth=5) dt.fit(train_data[feature_cols], train_data[target_col]) # 对测试数据进行预测 y_pred = dt.predict(test_data[feature_cols]) # 计算评估指标 accuracy = accuracy_score(test_data[target_col], y_pred) precision = precision_score(test_data[target_col], y_pred, average='macro') recall = recall_score(test_data[target_col], y_pred, average='macro') f1 = f1_score(test_data[target_col], y_pred, average='macro') print('Accuracy:', accuracy) print('Precision:', precision) print('Recall:', recall) print('F1 Score:', f1)ValueError: could not convert string to float: 'vhigh'

from sklearn.preprocessing import LabelEncoder le = LabelEncoder() for col in feature_cols: train_data[col] = le.fit_transform(train_data[col]) test_data[col] = le.transform(test_data[col]) 这...

这个代码为什么输出有问题import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score # 读取数据集 data = pd.read_csv('adult.csv') # 将数据集中的缺失值用平均值进行填充 data = data.fillna(data.mean()) # 将分类变量进行独热编码 data = pd.get_dummies(data) # 将目标变量进行二元编码 data['income'] = data['income'].apply(lambda x: 1 if x == '>50K' else 0) # 将数据集划分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(data.drop('income', axis=1), data['income'], test_size=0.2, random_state=42) # 对数据集进行标准化处理 scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) # 使用决策树算法建立分类模型 clf = DecisionTreeClassifier() clf.fit(X_train, y_train) # 对测试集进行预测 y_pred = clf.predict(X_test) # 计算模型的准确率、精确率、召回率和F1值 accuracy = accuracy_score(y_test, y_pred) precision = precision_score(y_test, y_pred) recall = recall_score(y_test, y_pred) f1 = f1_score(y_test, y_pred) # 输出模型的评估结果 print('Accuracy:', accuracy) print('Precision:', precision) print('Recall:', recall) print('F1 Score:', f1) # 将数据集保存为csv文件 data.to_csv('adult_processed.csv', index=False)

代码本身没有明显的语法错误，但是有可能是数据集中存在缺失值导致的。在第6行中，使用平均值填充了缺失值，但是并没有判断数据集中是否存在缺失值。如果数据集中不存在缺失值，那么填充操作将会导致数据集中出现非...

import pandas as pd data = pd.read_csv('adult.data', header=None) # 数据预处理 # 去除缺失值 data = data.dropna() # 数据类型转换 data[[0, 2, 4, 10, 11, 12]] = data[[0, 2, 4, 10, 11, 12]].apply(pd.to_numeric) # 特征选择 selected_features = [0, 2, 4, 10, 11, 12, 1, 3, 5, 6, 7, 8, 9] data = data[selected_features] # 数据标准化 from sklearn.preprocessing import StandardScaler scaler = StandardScaler() data[[0, 2, 10, 11, 12]] = scaler.fit_transform(data[[0, 2, 10, 11, 12]]) # 数据编码 data = pd.get_dummies(data) # 将数据集分为训练集和测试集 from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(data.drop(['<=50K', '>50K'], axis=1), data['<=50K'], test_size=0.2, random_state=42) # 使用决策树算法对训练集进行训练 from sklearn.tree import DecisionTreeClassifier clf = DecisionTreeClassifier() clf.fit(X_train, y_train) # 对测试集进行预测，计算准确率和召回率 from sklearn.metrics import accuracy_score, recall_score y_pred = clf.predict(X_test) accuracy = accuracy_score(y_test, y_pred) recall = recall_score(y_test, y_pred) # 输出分类结果 print('Accuracy:', accuracy) print('Recall:', recall)

这段代码使用了机器学习中的决策树算法来对成年人收入进行分类，将数据集分为训练集和测试集后，使用训练集进行模型训练，然后使用测试集进行预测，并计算准确率和召回率。其中，数据预处理包括去除缺失值、数据类型...

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.tree import DecisionTreeClassifier from sklearn.svm import SVC from sklearn.metrics import accuracy_score #读取数据 data = pd.read_csv('JD_消费者数据20180201-20180415.csv') #删除不需要的列 data = data.drop(['customer_id', 'product_id', 'action_date', 'action_id'], axis=1) #处理时间数据，将日期转为距离当前日期的天数 data['customer_register_date'] = (pd.to_datetime('2018-04-15') - pd.to_datetime(data['customer_register_date'])).dt.days data['product_market_date'] = (pd.to_datetime('2018-04-15') - pd.to_datetime(data['product_market_date'])).dt.days data['shop_register_date'] = (pd.to_datetime('2018-04-15') - pd.to_datetime(data['shop_register_date'])).dt.days #删除缺失值所在的行 data.dropna(inplace=True) #将分类变量转为数值变量，使用One-hot编码 data = pd.get_dummies(data, columns=['age_range', 'gender', 'brand', 'category', 'shop_category']) #将目标变量转为数值变量，PageView为0，Order为1 data['type'] = data['type'].apply(lambda x: 0 if x == 'PageView' else 1) #读取前五行 data.head(5)

这段代码使用了Python中的Pandas、NumPy、Matplotlib、Seaborn和Scikit-learn库来读取一个csv文件并对数据进行处理。其中，数据处理部分包括删除不需要的列、将时间数据转为距离当前日期的天数、删除缺失值所在的行...

import pandas as pd.docx

相关推荐

pd_split_train_test.rar_pandas_pandas对数据分类_pd.split_split_数据分类

Python库 | sklearn_cv_pandas-0.0.4.tar.gz

详解pandas库pd.read_excel操作读取excel文件参数整理与实例

import pandas as pd from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score import numpy as np data = pd.read_csv('iris.csv', header=None, names=['sepald

import pandas as pd.docx

最新推荐

node-v4.1.0-linux-x64.tar.xz

基于AT89S52的数字温度计设计说明.docx

zigbee-cluster-library-specification

管理建模和仿真的文件

实现实时数据湖架构：Kafka与Hive集成

spring添加xml配置文件

JSBSim Reference Manual

"互动学习：行动中的多样性与论文攻读经历"

实现实时监控告警系统：Kafka与Grafana整合

输出这段Python代码输出所有3位整数中，个位是5且是3的倍数的整数