如何使用Python Scikit-learn构建朴素贝叶斯分类器？

基于贝叶斯定理的朴素贝叶斯分类是根据未知数据集预测类别的过程。Scikit-learn有三个朴素贝叶斯模型，分别是：

  • 高斯朴素贝叶斯
  • 伯努利朴素贝叶斯
  • 多项式朴素贝叶斯

在本教程中，我们将学习使用Python Scikit-learn (Sklearn)构建高斯朴素贝叶斯和伯努利朴素贝叶斯分类器。

高斯朴素贝叶斯分类器

高斯朴素贝叶斯分类器基于以均值和方差为特征的连续分布。

让我们通过一个例子看看如何使用Scikit-Learn Python ML库来构建高斯朴素贝叶斯分类器。

在这个例子中，我们将使用高斯朴素贝叶斯模型，该模型假设每个标签的数据都来自简单的正态分布。我们将使用的数据集是威斯康星州乳腺癌诊断数据库。

示例

# Importing the necessary packages
import sklearn
from sklearn.datasets import load_breast_cancer

# Loading the dataset and organizing the data
DataSet = load_breast_cancer()
labelnames = DataSet['target_names']
labels = DataSet['target']
featurenames = DataSet['feature_names']
features = DataSet['data']

# Organizing dataset into training and testing set
# by using train_test_split() function
from sklearn.model_selection import train_test_split
train, test, train_labels, test_labels = train_test_split(features,labels,test_size = 0.30, random_state = 300)

# Model evaluation by using Naïve Bayes algorithm.
from sklearn.naive_bayes import GaussianNB

# Let's initializing the model:
NBclassifier = GaussianNB()

# Train the model:
NBmodel = NBclassifier.fit(train, train_labels)

# Making predictions by using pred() function:
NBpreds = NBclassifier.predict(test)
print("The predictions are:\n", NBpreds[:15])

# Finding accuracy of our Naive Bayes classifier:
from sklearn.metrics import accuracy_score
print("Accuracy of our classifier is:", accuracy_score(test_labels, NBpreds) *100)

输出

它将产生以下输出：

The predictions are:
[0 0 1 1 0 0 0 1 1 1 1 1 0 1 0]
Accuracy of our classifier is: 93.56725146198829

伯努利朴素贝叶斯分类器

伯努利朴素贝叶斯分类器是一种二元算法。当我们需要检查特征是否存在时，它非常有用。

让我们通过一个例子看看如何使用Scikit-Learn Python ML库来构建伯努利朴素贝叶斯分类器。

示例

在下面的例子中，我们将使用scikit-learn python库在一个虚拟数据集上实现伯努利朴素贝叶斯算法。

from sklearn.datasets import make_classification
# Importing libraries
from sklearn.datasets import make_classification
import matplotlib.pyplot as plt

# Creating the classification dataset with one informative feature and one cluster per class
nb_samples = 300
X, Y = make_classification(n_samples=nb_samples, n_features=2, n_informative=2, n_redundant=0)

# Plotting the dataset
plt.figure(figsize=(7.50, 3.50))
plt.subplots_adjust(bottom=0.05, top=0.9, left=0.05, right=0.95)
plt.subplot(111)
plt.scatter(X[:, 0], X[:, 1], marker="o", c=Y, s=40, edgecolor="k")
plt.show()

输出

我们将获得以下虚拟数据集：

示例

现在，让我们在这个虚拟数据集上构建伯努利朴素贝叶斯分类器：

# Importing libraries
from sklearn.datasets import make_classification
import numpy as np

# Model evaluation by using Bernoulli Naïve Bayes algorithm.

# Import Bernoulli Naive bayes from sklearn
from sklearn.naive_bayes import BernoulliNB

# Organizing dataset into training and testing set
# by using train_test_split() function
from sklearn.model_selection import train_test_split

# Creating the classification dataset with one informative feature and one cluster per class
nb_samples = 300
X, Y = make_classification(n_samples=nb_samples, n_features=2, n_informative=2, n_redundant=0)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.30)

# Let's initializing the model
B_NaiveBayes = BernoulliNB(binarize=0.0)

# Train the model
B_NaiveBayes.fit(X_train, Y_train)

# Making predictions by using pred() function
data = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
Preds=B_NaiveBayes.predict(data)
print(Preds)

输出

它将产生以下输出：

array([0, 0, 1, 1])

更新于：2022年10月4日

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