我使用逻辑回归训练了一个模型，现在需要用Log Loss来评估其准确性。以下是数据的一些细节：
特征/ X
Principal terms age Gender weekend Bachelor HighSchoolerBelow college0 1000 30 45 0 0 0 1 01 1000 30 33 1 0 1 0 02 1000 15 27 0 0 0 0 1标签/ Y
array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'COLLECTION'], dtype=object)逻辑回归模型：
from sklearn.linear_model import LogisticRegressionlogreg = LogisticRegression(C=1e5, solver='lbfgs', multi_class='multinomial')Feature = df[['Principal','terms','age','Gender','weekend']]Feature = pd.concat([Feature,pd.get_dummies(df['education'])], axis=1)Feature.drop(['Master or Above'], axis = 1,inplace=True)X = FeatureX= preprocessing.StandardScaler().fit(X).transform(X)y = df['loan_status'].valuesX_train, X_test, y_train, lg_y_test = train_test_split(X, y, test_size=0.3, random_state=4)# we create an instance of Neighbours Classifier and fit the data.logreg.fit(X_train, y_train)lg_loan_status = logreg.predict(X_test)lg_loan_status现在我需要计算Jaccard, F1-score和LogLoss。
这是我的单独测试数据集：
test_df['due_date'] = pd.to_datetime(test_df['due_date'])test_df['effective_date'] = pd.to_datetime(test_df['effective_date'])test_df['dayofweek'] = test_df['effective_date'].dt.dayofweektest_df['weekend'] = test_df['dayofweek'].apply(lambda x: 1 if (x>3) else 0)test_df.groupby(['Gender'])['loan_status'].value_counts(normalize=True)# test_df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)Feature = test_df[['Principal','terms','age','Gender','weekend']]Feature = pd.concat([Feature,pd.get_dummies(df['education'])], axis=1)Feature.drop(['Master or Above'], axis = 1,inplace=True)Feature.head()X = FeatureY = test_df['loan_status'].valuesFeature.head() Principal terms age Gender weekend Bechalor HighSchoolorBelow college0 1000.0 30.0 50.0 female 0.0 0 1 01 300.0 7.0 35.0 male 1.0 1 0 02 1000.0 30.0 43.0 female 1.0 0 0 1这是我尝试过的方法：
# Evaluation for Logistic RegressionX_train, X_test, y_train, lg_y_test = train_test_split(X, y, test_size=0.3, random_state=3)lg_jaccard = jaccard_similarity_score(lg_y_test, lg_loan_status, normalize=False)lg_f1_score = f1_score(lg_y_test, lg_loan_status, average='micro')lg_log_loss = log_loss(lg_y_test, lg_loan_status)print('Jaccard is : {}'.format(lg_jaccard))print('F1-score is : {}'.format(lg_f1_score))print('Log Loss is : {}'.format(lg_log_loss))但它返回了以下错误：
ValueError: could not convert string to float: ‘COLLECTION’
更新：这是lg_y_test：
['PAIDOFF' 'PAIDOFF' 'COLLECTION' 'COLLECTION' 'PAIDOFF' 'COLLECTION''PAIDOFF' 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'COLLECTION' 'COLLECTION' 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'COLLECTION' 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'COLLECTION' 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'COLLECTION']回答：
问题如下：
要计算log_loss，你需要得到预测的概率。如果你只提供了预测的类别（即概率最高的类别），这个指标是无法计算的。
Sklearn提供了predict_proba方法，只要可能就应该使用它，如下所示：
lg_loan_status_probas = logreg.predict_proba(X_test)lg_log_loss = log_loss(lg_y_test, lg_loan_status_probas)