In [32]:
from sklearn import datasets
import numpy as np
from sklearn.linear_model import LogisticRegression
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
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iris = datasets.load_iris()
x = iris['data'][:,3:] #patal width
y = (iris['target']==2).astype(np.int)
In [34]:
logit_reg = LogisticRegression()
logit_reg.fit(x, y)
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In [35]:
plt.plot(x)
plt.figure()
plt.plot(iris['target'])
plt.figure()
plt.plot(x, y, "g-", label="Iris-Viginica")
# plt.plot(x, y, "b--", label="None Iris-Viginica")
Out[35]:
In [36]:
x_new = np.linspace(0,3,1000).reshape(-1,1)
y_prob = logit_reg.predict_proba(x_new)
decision_boundary = x_new[y_prob[:,1]>=0.5][0]
print(decision_boundary)
In [37]:
plt.plot(x_new, y_prob[:,1], "g-", label="Iris-Viginica")
plt.plot(x_new, y_prob[:,0], "b--", label="None Iris-Viginica")
Out[37]:
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