さまざまな性能評価指標

混合行列

Coding

code: Python

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import LabelEncoder

# yのカテゴリ変数「M」「B」を数値に変換する

le = LabelEncoder()

y = le.fit_transform(y)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, stratify=y, random_state=1)

from sklearn.pipeline import make_pipeline

from sklearn.preprocessing import StandardScaler

from sklearn.svm import SVC

from sklearn.metrics import confusion_matrix

pipe_svc = make_pipeline(StandardScaler(), SVC(random_state=1))

pipe_svc.fit(X_train, y_train)

y_pred = pipe_svc.predict(X_test)

# テストと予測のデータから混合行列を生成

confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)

print(confmat)

%matplotlib inline

import matplotlib.pyplot as plt

# 図のサイズを指定

fig, ax = plt.subplots(figsize=(2.5, 2.5))

# matshow関数で行列からヒートマップを描画

ax.matshow(confmat, cmap=plt.cm.Blues, alpha=0.3)

plt.xlabel('predicted label')

plt.ylabel('true label')

plt.tight_layout()

plt.show()

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https://gyazo.com/6eaf3869293cc3e55c3f991abdfbd6aa

適合率、再現率、F1スコアを出力

Coding

code: Python

from sklearn.metrics import precision_score

from sklearn.metrics import recall_score, f1_score

print('Precision: {:.3f}'.format(precision_score(y_true=y_test, y_pred=y_pred)))

print('Recall: {:.3f}'.format(recall_score(y_true=y_test, y_pred=y_pred)))

print('F1: {:.3f}'.format(f1_score(y_true=y_test, y_pred=y_pred)))

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Precision: 0.976

Recall: 0.952

F1: 0.964

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