我正在嘗試構(gòu)建模型并創(chuàng)建網(wǎng)格搜索，下面是代碼。原始數(shù)據(jù)是從該網(wǎng)站下載的（信用卡欺詐數(shù)據(jù)）。 https://www.kaggle.com/mlg-ulb/creditcardfraud讀取數(shù)據(jù)后從標(biāo)準(zhǔn)化開始編碼。standardization = StandardScaler()credit_card_fraud_df[['Amount']] = standardization.fit_transform(credit_card_fraud_df[['Amount']])# Assigning feature variable to XX = credit_card_fraud_df.drop(['Class'], axis=1)# Assigning response variable to yy = credit_card_fraud_df['Class']# Splitting the data into train and testX_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, test_size=0.3, random_state=100)X_train.head()power_transformer = PowerTransformer(copy=False)power_transformer.fit(X_train)                       ## Fit the PT on training dataX_train_pt_df = power_transformer.transform(X_train)    ## Then apply on all dataX_test_pt_df = power_transformer.transform(X_test)y_train_pt_df = y_trainy_test_pt_df = y_testtrain_pt_df = pd.DataFrame(data=X_train_pt_df, columns=X_train.columns.tolist())# set up cross validation schemefolds = StratifiedKFold(n_splits = 5, shuffle = True, random_state = 4)# specify range of hyperparametersparams = {"C":np.logspace(-3,3,5,7), "penalty":["l1","l2"]}# l1 lasso l2 ridge結(jié)果示例：  mean_fit_time std_fit_time    mean_score_time std_score_time  param_C param_penalty   params  split0_test_score   split1_test_score   split2_test_score   split3_test_score   split4_test_score   mean_test_score std_test_score  rank_test_score    0   0.044332    0.002040    0.000000    0.000000    0.001   l1  {'C': 0.001, 'penalty': 'l1'}   NaN NaN NaN NaN NaN NaN NaN 6    1   0.477965    0.046651    0.016745    0.003813    0.001   l2  {'C': 0.001, 'penalty': 'l2'}   0.485714    0.428571    0.542857    0.485714    0.457143    0.480000    0.037904    5我的輸入數(shù)據(jù)中沒有任何空值。我不明白為什么我會(huì)得到這些列的 Nan 值。誰(shuí)能幫幫我嗎？