Подсчитайте метрики для модели кредитного скоринга. Используйте переменные:
- y_pred — предсказания модели;
- y_test — реальные значения целевой переменной.
import pandas as pd
import seaborn as sns
RANDOM_STATE = 42
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
# добавьте необходимые импорты
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
# загрузим данные
df = pd.read_csv('car_loan.csv')
X = df.drop(columns='default')
y = df['default']
X_train, X_test, y_train, y_test = train_test_split(
X,
y,
random_state=RANDOM_STATE
)
scaler = StandardScaler()
X_train_scalled = scaler.fit_transform(X_train)
X_test_scalled = scaler.transform(X_test)
clf = LogisticRegression()
clf = clf.fit(X_train_scalled, y_train)
y_pred = clf.predict(X_test_scalled)
recall = recall_score(y_test, y_pred) # здесь ваш код
precision = precision_score(y_test, y_pred) # здесь ваш код
print('recall: ',round(recall ,3))
print('precision: ',round(precision,3))