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import numpy as np
import matplotlib.pyplot as pltfrom sklearn.model_selection import train_test_split
from sklearn.datasets import make_classificationnp.random.seed(0)
X = np.random.normal(0,1,size=(200,2))
y = np.array((X[:,0]**2)+(X[:,1]**2)<2, dtype='int')
yarray([0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0,
1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1,
0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1,
1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1,
1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0,
1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1,
0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0,
0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1,
0, 0])x_train, x_test, y_train, y_test = train_test_split(X, y, train_size = 0.7, random_state = 233, stratify = y)plt.scatter(x_train[:,0], x_train[:,1], c = y_train)
plt.show()
from sklearn.linear_model import LogisticRegressionclf = LogisticRegression()
clf.fit(x_train, y_train)LogisticRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LogisticRegression()
clf.score(x_train, y_train)0.7071428571428572
clf.score(x_test, y_test)0.6666666666666666
from sklearn.preprocessing import PolynomialFeaturespoly = PolynomialFeatures(degree=2)
poly.fit(x_train)PolynomialFeatures()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
PolynomialFeatures()
x2 = poly.transform(x_train)
x2t = poly.transform(x_test)clf.fit(x2, y_train)LogisticRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LogisticRegression()
clf.score(x2, y_train)1.0
clf.score(x2t, y_test)0.9666666666666667
from sklearn import datasets
iris = datasets.load_iris()
x = iris.data
y = iris.target
x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=666)plt.scatter(x_train[:,0], x_train[:,1], c=y_train)
plt.show()
from sklearn.multiclass import OneVsRestClassifier
ovr = OneVsRestClassifier(clf)
ovr.fit(x_train,y_train)
ovr.score(x_test, y_test)0.9736842105263158
from sklearn.multiclass import OneVsOneClassifier
ovr = OneVsOneClassifier(clf)
ovr.fit(x_train,y_train)
ovr.score(x_test, y_test)1.0
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