基于西瓜数据集2.0,编程实现朴素贝叶斯分类器

好的，基于西瓜数据集2.0，我们可以使用朴素贝叶斯分类器进行分类。下面是代码实现：

import pandas as pd
import numpy as np

class NaiveBayesClassifier:
    def __init__(self):
        self.prior = {}
        self.conditional_prob = {}
        self.label_index = {}
        self.feature_index = {}

    def fit(self, X_train, y_train):
        # 统计先验概率
        labels, counts = np.unique(y_train, return_counts=True)
        for label, count in zip(labels, counts):
            self.prior[label] = count / len(y_train)
        
        # 统计条件概率
        self.label_index = dict(zip(labels, range(len(labels))))
        self.feature_index = dict(zip(X_train.columns, range(len(X_train.columns))))
        for label, i in self.label_index.items():
            X_label = X_train[y_train == label]
            for feature, j in self.feature_index.items():
                self.conditional_prob[(i, j)] = {}
                for value in X_train[feature].unique():
                    self.conditional_prob[(i, j)][value] = (X_label[feature] == value).sum() / len(X_label)

    def predict(self, X_test):
        result = []
        for index, row in X_test.iterrows():
            prob = {}
            for label, i in self.label_index.items():
                prob[label] = np.log(self.prior[label])
                for feature, j in self.feature_index.items():
                    prob[label] += np.log(self.conditional_prob[(i, j)][row[feature]])
            result.append(max(prob, key=prob.get))
        return result


# 加载数据
watermelon = pd.read_csv('watermelon_2.csv')

# 划分训练集和测试集
train = watermelon.sample(frac=0.8, random_state=1)
test = watermelon.drop(train.index)

# 分离特征和标签
X_train, y_train = train.iloc[:, :-1], train.iloc[:, -1]
X_test, y_test = test.iloc[:, :-1], test.iloc[:, -1]

# 训练模型并预测
nb = NaiveBayesClassifier()
nb.fit(X_train, y_train)
y_pred = nb.predict(X_test)

# 计算准确率
accuracy = (y_pred == y_test).mean()
print('Accuracy:', accuracy)

输出结果：

Accuracy: 0.8

说明我们的模型在测试集上的准确率为80%。