Xg3-3-2-1

from sklearn.datasets import load_iris

ds_iris = load_iris()

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Xg3-3-2-2

ds_iris.data

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Xg3-3-2-3

ds_iris.data.shape

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Xg3-3-2-4

ds_iris.target

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Xg3-3-2-5

from sklearn.model_selection import train_test_split

features_train, features_test, label_train, label_test \
  = train_test_split(ds_iris.data, ds_iris.target, test_size=0.3)

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print(features_train.shape)
print(label_train.shape)
print(features_test.shape)
print(label_test.shape)

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features_train

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Xg3-3-2-6

from sklearn.svm import SVC

svc = SVC()

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Xg3-3-2-7

svc.fit(features_train, label_train)

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Xg3-3-2-8

label_pred = svc.predict(features_test)

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label_pred

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label_test

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Xg3-3-2-9

from sklearn.metrics import accuracy_score

accuracy_score(label_test, label_pred)

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Xg3-3-2-10

import numpy as np

svc.predict(np.array([[7, 3, 6, 2]]))

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