sklearn學習筆記

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

from sklearn.feature_extraction import dictvectorizer

from sklearn.feature_extraction.text import countvectorizer

#sklearn 資料呼叫

def dict_demo():

#字典特徵提取

data = [,,]

#1,例項化乙個轉換器

transfer = dictvectorizer(sparse=false)

data_new=transfer.fit_transform(data)

print(「data_new: \n」,data_new)

print(「data_new: \n」, transfer.get_feature_names())

def dictvec():

dict=dictvectorizer(sparse=false)

s = [,,]

data=dict.fit_transform(s)

s = [{}]

name=dict.get_feature_names()

print(name)

print(data)

def count_demo():

#文字特徵提取

#例項化乙個轉換器類

data = ["life is short ,i like python","life is too long ,i dislike python "]
#呼叫fit_transform
transfer = countvectorizer()
datanew = transfer.fit_transform(data)
print("datanew:\n",datanew.toarray())

# 漢字文字特徵提取

# 例項化乙個轉換器類

data = ["我 愛 北 京 天 安 門", "我 愛 我 家 "]
# 呼叫fit_transform
transfer = countvectorizer()
datanew = transfer.fit_transform(data)
print("datanew:\n", datanew)
return none

count_demo()

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