from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix
import numpy as np
x = iris_df[['花萼长度 (cm)','花萼宽度(cm)','花瓣长度 (cm)','花瓣宽度 (cm)']]
y = iris_df['target']
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)
model = LogisticRegression(max_iter=200)
model.fit(x_train, y_train)
predictions = model.predict(x_test)
print(classification_report(y_test, predictions))
print('\n混淆矩阵:')
print(confusion_matrix(y_test, predictions))
以下为主要考点模型
from sklearn.naive_bayes import GaussianNB
model =GaussianNB()
model.fit(x_train,y_train)
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(x_train,y_train)
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
model.fit(x_train,y_train)
from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier()
model.fit(x_train,y_train)
导入k近邻模型
from sklearn.neighbors import KNeighborsClassifier
model = KNeighborsClassifier()
model.fit(x_train,y_train)
模型预测与评估
y_test_pred = model.predict(x_test)
print(y_test_pred)
测试准确度/精确度,并输出
accuracy = model.score(x_test,y_test)
print(accuracy)