其他方法python代码

好的，以下是使用决策树和朴素贝叶斯算法实现的玻璃类型识别的Python代码示例，供参考：

决策树：

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

# 读取数据集
data = pd.read_csv('glass.csv')

# 划分训练集和测试集
X = data.iloc[:, :-1]
y = data.iloc[:, -1]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 模型训练
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)

# 模型预测
y_pred = dt.predict(X_test)

# 模型评估
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)

朴素贝叶斯：

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score

# 读取数据集
data = pd.read_csv('glass.csv')

# 划分训练集和测试集
X = data.iloc[:, :-1]
y = data.iloc[:, -1]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 模型训练
nb = GaussianNB()
nb.fit(X_train, y_train)

# 模型预测
y_pred = nb.predict(X_test)

# 模型评估
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)

需要注意的是，以上示例仅供参考，具体的实现需要结合数据集的实际情况来确定。此外，还可以使用支持向量机（SVM）等算法来实现玻璃类型识别。