#🐍 #Python #評価指標 #混同行列 #正解率 #適合率 #再現率 #F値 #自作関数

evaluations_classfier_basic.py

from sklearn.metrics import accuracy_score # 正解率
from sklearn.metrics import precision_score # 適合率
from sklearn.metrics import recall_score # 再現率
from sklearn.metrics import f1_score # F値

def evaluations(y_test, y_predict, average) -> dict: # 辞書型で返す
    """
    y_test: 
      予測したいデータ
    y_predict:
      アルゴリズムで予測したデータ
    average:
      macro/micro/
    """
    accuracy = accuracy_score(test, predict)
    precision = precision_score(test, predict, average=average)
    recall = recall_score(test, predict, average=average)
    f1 = f1_score(test, predict, average=average)
    evaluations = {
        "正解率" : round(accuracy, 3), 
        "適合率" : round(precision, 3),
        "再現率" : round(recall, 3), 
        "F値" : round(f1, 3)
    }
    return evaluations

PandasのDataFrame型にすれば綺麗に見えるよ