朴素贝叶斯算法python实现

朴素贝叶斯算法是一种基于贝叶斯定理的机器学习算法，它在分类和预测问题中被广泛应用。下面是朴素贝叶斯算法的Python实现。

首先，我们需要导入必要的库：

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

然后，我们加载数据集并进行预处理：

# Load iris dataset
iris = datasets.load_iris()

# Convert iris dataset to pandas dataframe
df = pd.DataFrame(data= np.c_[iris['data'], iris['target']],
                  columns= iris['feature_names'] + ['target'])

# Split dataset into features and labels
X = df.drop('target', axis=1)
y = df['target']

# Split dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

接下来，我们使用GaussianNB类来训练模型：

# Train the model
model = GaussianNB()
model.fit(X_train, y_train)

最后，我们使用测试集来评估模型的性能：

# Make predictions on test set
y_pred = model.predict(X_test)

# Calculate accuracy score
accuracy = accuracy_score(y_test, y_pred)

print('Accuracy:', accuracy)

完整的代码如下：

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

# Load iris dataset
iris = datasets.load_iris()

# Convert iris dataset to pandas dataframe
df = pd.DataFrame(data= np.c_[iris['data'], iris['target']],
                  columns= iris['feature_names'] + ['target'])

# Split dataset into features and labels
X = df.drop('target', axis=1)
y = df['target']

# Split dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train the model
model = GaussianNB()
model.fit(X_train, y_train)

# Make predictions on test set
y_pred = model.predict(X_test)

# Calculate accuracy score
accuracy = accuracy_score(y_test, y_pred)

print('Accuracy:', accuracy)