
import sklearn
from sklearn import datasets
from sklearn import svm

# We will load a caner DataSet
cancer = datasets.load_breast_cancer()

x = cancer.data
y = cancer.target

x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(x, y , test_size=0.2)

print(x_train, y_train)
classes = ['malignant','benign']
28e6391371d1e951d0f410b0154fbb5bb2473752
<mask token>

<mask token>
print(x_train, y_train)
<mask token>

<mask token>
cancer = datasets.load_breast_cancer()
x = cancer.data
y = cancer.target
x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(x,
    y, test_size=0.2)
print(x_train, y_train)
classes = ['malignant', 'benign']

import sklearn
from sklearn import datasets
from sklearn import svm
cancer = datasets.load_breast_cancer()
x = cancer.data
y = cancer.target
x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(x,
    y, test_size=0.2)
print(x_train, y_train)
classes = ['malignant', 'benign']


import sklearn
from sklearn import datasets
from sklearn import svm

# We will load a caner DataSet
cancer = datasets.load_breast_cancer()

x = cancer.data
y = cancer.target

x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(x, y , test_size=0.2)

print(x_train, y_train)
classes = ['malignant','benign']