基于最小错误率的贝叶斯决策python

可以使用Python中的numpy、pandas和sklearn库实现基于最小错误率的贝叶斯决策。

首先，加载数据集并进行数据预处理，将数据拆分为训练数据和测试数据：

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split

data = pd.read_csv('data_set.csv')
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

然后，计算各类别的先验概率和条件概率：

def calculate_prior_probability(y_train):
    values, counts = np.unique(y_train, return_counts=True)
    return counts / len(y_train)

def calculate_conditional_probability(X_train, y_train):
    total_classes = np.unique(y_train)
    total_features = X_train.shape[1]
    conditional_probability = np.zeros((len(total_classes), total_features, 2))

    for i in range(len(total_classes)):
        X_train_i = X_train[y_train == total_classes[i]]
        for j in range(total_features):
            conditional_probability[i][j][0] = len(X_train_i[X_train_i[:, j] < 0])/len(X_train_i)
            conditional_probability[i][j][1] = len(X_train_i[X_train_i[:, j] >= 0])/len(X_train_i)

    return conditional_probability

接着，使用贝叶斯决策进行分类：

def bayes_decision(X_test, prior_probability, conditional_probability):
    y_pred = []
    total_classes = len(prior_probability)
    total_samples = X_test.shape[0]

    for i in range(total_samples):
        samples_prob = np.zeros(total_classes)
        for j in range(total_classes):
            likelihood = 1.0
            for k in range(X_test.shape[1]):
                likelihood *= conditional_probability[j][k][(X_test[i][k] >= 0).astype(int)]
            samples_prob[j] = likelihood * prior_probability[j]
        y_pred.append(np.argmax(samples_prob))

    return y_pred

最后，计算模型的预测准确率：

prior_probability = calculate_prior_probability(y_train)
conditional_probability = calculate_conditional_probability(X_train, y_train)
y_pred = bayes_decision(X_test, prior_probability, conditional_probability)
accuracy = np.sum(y_pred == y_test)/len(y_test)
print("Accuracy: ", accuracy)