問題文
問題の概要
適合率，再現率，F1スコアはそれぞれ「precision_score()」「recall_score()」「f1_score()」で計算できます。「average」には「'micro'」「'macro'」などが指定可能*1です。
import pandas as pd import joblib from sklearn.metrics import recall_score, precision_score, f1_score X_train = pd.read_table('ch06/train.feature.txt', header=None) X_test = pd.read_table('ch06/test.feature.txt', header=None) y_train = pd.read_table('ch06/train.txt', header=None)[1] y_test = pd.read_table('ch06/test.txt', header=None)[1] clf = joblib.load('ch06/model.joblib') y_pred = clf.predict(X_test) print(f'test recall of None: {recall_score(y_test, y_pred, average=None)}') print(f'test recall of micro: {recall_score(y_test, y_pred, average="micro")}') print(f'test recall of macro: {recall_score(y_test, y_pred, average="macro")}') print(f'test precision of None: {precision_score(y_test, y_pred, average=None)}') print(f'test precision of micro: {precision_score(y_test, y_pred, average="micro")}') print(f'test precision of macro: {precision_score(y_test, y_pred, average="macro")}') print(f'test f1 of None: {f1_score(y_test, y_pred, average=None)}') print(f'test f1 of micro: {f1_score(y_test, y_pred, average="micro")}') print(f'test f1 of macro: {f1_score(y_test, y_pred, average="macro")}')
