In [1]:
# load Breast Cancer Wisconsin dataset
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
df = pd.read_csv('https://raw.githubusercontent.com/rasbt/python-machine-learning-book/master/code/datasets/wdbc/wdbc.data', header=None)
X = df.loc[:, 2:].values
y = df.loc[:, 1].values
le = LabelEncoder()
y = le.fit_transform(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=1)
In [2]:
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
# construct pipeline for SVC
pipe_svc = Pipeline([('scl', StandardScaler()),
('clf', SVC(random_state=1))])
In [3]:
from sklearn.metrics import confusion_matrix
# train the model
pipe_svc.fit(X_train, y_train)
# obtain predicted label
y_pred = pipe_svc.predict(X_test)
# print confusion matrix
confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)
print(confmat)
In [5]:
# plot the confusion matrix illustration using matplotlib's matshow function
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(2.5, 2.5))
ax.matshow(confmat, cmap=plt.cm.Blues, alpha=0.3)
for i in range(confmat.shape[0]):
for j in range(confmat.shape[1]):
ax.text(x=j, y=i, s=confmat[i, j], va='center', ha='center')
plt.xlabel('predicted label')
plt.ylabel('true label')
plt.show()