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Machine Learning notes
skearn的非数值进行数值性编码与sklearn的库进行准确率的计算
1.可以使用python自带的skearn对result的非数值进行数值性编码
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| from sklearn.model_selection import train_test_split
y = LabelEncoder().fit_transform(data[4])
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2.可以使用sklearn的库进行准确率的计算
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| from sklearn.metrics import accuracy_socre
from sklearn.preprocessing import LabelEncoder
from sklearn.tree import DecisionTreeClassifier
x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.7, random_state=1)
model = DecisionTreeClassifier(criterion='entorpy')
model.fit(x_train,y_train)
y_test_hat = model.predict(x_test)
print("accuracy_score",accuracy_socre(y_test_hat,y_test))
y_test = y_test.reshape(-1)
result = (y_test_hat==y_test)
acc = np.mean(result)
print(f"准确率:{100*acc}")
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