打卡第十天

机器学习建模与评估

知识点： 1.数据集的划分 2.机器学习模型建模的三行代码 3.机器学习模型分类问题的评估 今日代码比较多，但是难度不大，仔细看看示例代码，好好理解下这几个评估指标。

作业：尝试对心脏病数据集采用机器学习模型建模和评估

1、数据集的划分

import pandas as pd
data = pd.read_csv(r'C:\Users\许兰\Desktop\打卡文件\python60-days-challenge-master\heart.csv')
from sklearn.model_selection import train_test_split
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)
print(f"训练集形状={X_train.shape},测试集形状={X_test.shape}")

过程：读取文件后，用sklearn中的方法划分训练集和测试集，x,y分别是特征和标签 ，将两者随机划分，并写下随机种子固定划分的数据。

2、机器学习的三行代码

svm_model = SVC(random_state = 42)
svm_model.fit(X_train, y_train)
svm_pred = svm_model.predict(X_test)

过程：模型实例化，模型训练需要带入训练集，然后进行模型预测需要带入测试集。

3、机器学习分类模型的评估

import pandas as pd
data = pd.read_csv(r'C:\Users\许兰\Desktop\打卡文件\python60-days-challenge-master\heart.csv')
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, precision_score, recall_score, f1_score
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)
print(f"训练集形状={X_train.shape},测试集形状={X_test.shape}")
from sklearn.svm import SVC
svm_model = SVC(random_state = 42)
svm_model.fit(X_train, y_train)
svm_pred = svm_model.predict(X_test)
print("\nSVM 分类报告：")
print(classification_report(y_test, svm_pred))
print("SVM 混淆矩阵：")
print(confusion_matrix(y_test, svm_pred))
svm_accuracy = accuracy_score(y_test, svm_pred)
svm_precision = precision_score(y_test, svm_pred)
svm_recall = recall_score(y_test, svm_pred)
svm_f1 = f1_score(y_test, svm_pred)
print("SVM 模型评估指标：")
print(f"准确率：{svm_accuracy:.4f}")
print(f"精确率：{svm_precision:.4f}")
print(f"召回率：{svm_recall:.4f}")

我只做了支持向量机这个模型的评估，但是其他模型和其处理流程几乎完全相同，不再赘述。

结果如下：

SVM 混淆矩阵：
[[15 14]
[ 4 28]]
SVM 模型评估指标：
准确率：0.7049
精确率：0.6667
召回率：0.8750

这里@浙大疏锦行