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- from sklearn.naive_bayes import GaussianNB
- from sklearn.model_selection import cross_val_score, train_test_split
- from sklearn.feature_extraction.text import CountVectorizer
- from datasets import Datasets
- import numpy as np
- # 提取特征 向量化 以2-gram
- def get_feature(x):
- cv = CountVectorizer(ngram_range=(2, 2), decode_error="ignore", token_pattern=r"\w", min_df=1)
- x = cv.fit_transform(x).toarray()
- return x
- def main():
- x, y = Datasets.load_dga_domain()
- x = get_feature(x)
- x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)
- gnb = GaussianNB()
- gnb.fit(x_train, y_train)
- print(gnb.score(x_test, y_test)) # 0.9422222222222222
- scores = cross_val_score(gnb, x, y, cv=3, scoring="accuracy")
- print(scores.mean()) # 0.9356666666666666
- if __name__ == "__main__":
- main()
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