Naive Bayes is a supervised learning algorithm commonly used in classification problems. It is based on Bayesian theorem, by calculating the prior probability and posterior probability to obtain the class probability, and then classify.
Naive Bayes assumes that the features are independent of each other, that is, the presence of one feature has no effect on the appearance of another feature. Although this assumption is difficult to implement in practical applications, it has shown good classification results in practice.
The algorithm flow is as follows:
1. Collect training data set
2. Transform the data set according to the rules to make it a standard data set
3. Calculate the prior probability of occurrence of each category
4. Calculate the conditional probability of each feature in each category
5. Classify new data, calculate its posterior probability under each category, and select the category with the largest posterior probability as the classification result
Compared with other algorithms, the naive Bayesian algorithm has faster calculation speed and better classification effect, but it assumes that the features are independent of each other, and the assumptions on the feature distribution are the same, which may lead to classification errors under certain conditions .
Here is a sample code to implement the Naive Bayes algorithm in Python:
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
# 加载iris数据集
iris = load_iris()
data = iris.data
target = iris.target
# 将数据集分成训练集和测试集
x_train, x_test, y_train, y_test = train_test_split(data, target, test_size=0.3)
# 创建高斯朴素贝叶斯模型
model = GaussianNB()
# 训练模型
model.fit(x_train, y_train)
# 预测测试集
y_pred = model.predict(x_test)
# 计算准确率
accuracy = sum(y_pred == y_test) / len(y_test)
print("准确率为：%.2f" % accuracy)This code uses the GaussianNB class in the sklearn library to implement Gaussian Naive Bayesian classification, and uses the classic iris dataset for testing. First divide the data set into training set and test set according to the ratio of 7:3, then use the training set to train the model, use the test set to make predictions, and calculate the accuracy of the algorithm on the test set