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使用SciKit Learn进行Logisitc回归

有人可以帮我调试这段代码吗?谢谢!

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn import linear_model
data = pd.read_csv('Mower.csv')
data = data.values
y = data[:,2]
x = data[:,:2]
y_train = y[:int(0.3*len(y))]
x_train = x[:int(0.3*len(y)),:]
y_validate = y[int(0.3*len((y))):]
x_validate = x[int(0.3*len((y))):,:]
clf = linear_model.LogisticRegression
clf.fit(x_train,y_train)
y_hat = clf.predict(x_validate)
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给我以下错误:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-77-a0a54feba3ef> in <module>()
      1 clf = linear_model.LogisticRegression
----> 2 clf.fit(x_train,y_train)
      3 y_hat = clf.predict(x_validate)

TypeError: unbound method fit() must be called with …
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python regression scikit-learn

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python ×1

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