每日一篇学习笔记 直到我做完专题 :( [Day39]

今天来自己重新做一个简单模型 虽然数据有点太少:l

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

data = {
\'重量\' : [150,130,180,160],
\'颜色\' : [\'red\',\'red\',\'orange\',\'orange\'],
\'品项\' : [\'苹果\',\'苹果\',\'橘子\',\'橘子\']
}

df = pd.DataFrame(data)
df[\'data_color\'] = pd.Series(data[\'颜色\']).map({\'red\':0,\'orange\':1})
df[\'data_item\'] = pd.Series(data[\'品项\']).map({\'苹果\':0,\'橘子\':1})
x = df[[\'重量\',\'data_color\']]
y = df[[\'品项\']]
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=1)

model = LogisticRegression()

model.fit(x_train,y_train)

y_pred = model.predict(x_test)

print("测试集的实际标籤:")
print(y_test.values)
print("模型的预测结果")
print(y_pred)

accuracy = accuracy_score(y_test,y_pred)
print(f"模型的準确率:{accuracy * 100:.2f}")

虽然测试集只有一个 还直接预测错那之后可以再改进