我正在嘗試構建模型并創建網格搜索，下面是代碼。原始數據是從該網站下載的（信用卡欺詐數據）。 https://www.kaggle.com/mlg-ulb/creditcardfraud讀取數據后從標準化開始編碼。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我的輸入數據中沒有任何空值。我不明白為什么我會得到這些列的 Nan 值。誰能幫幫我嗎？
1 回答
ITMISS
TA貢獻1871條經驗 獲得超8個贊
您在此處定義的默認求解器有問題：
model = LogisticRegression(class_weight='balanced')
這是從以下錯誤消息得出的：
ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.
此外，在定義參數網格之前研究文檔可能會很有用：
penalty: {'l1', 'l2', 'elasticnet', 'none'}, default='l2' 用于指定懲罰中使用的范數?！皀ewton-cg”、“sag”和“lbfgs”求解器僅支持 l2 懲罰?！癳lasticnet”僅受“saga”求解器支持。如果為“none”（liblinear 求解器不支持），則不應用正則化。
一旦您使用支持所需網格的不同解算器糾正它，您就可以開始：
## using Logistic regression for class imbalance
model = LogisticRegression(class_weight='balanced', solver='saga')
grid_search_cv = GridSearchCV(estimator = model, param_grid = params,
scoring= 'roc_auc',
cv = folds,
return_train_score=True, verbose = 1)
grid_search_cv.fit(X_train_pt_df, y_train_pt_df)
## reviewing the results
cv_results = pd.DataFrame(grid_search_cv.cv_results_)
另請注意，ConvergenceWarning這可能建議您需要增加默認值max_iter、tol或切換到另一個求解器并重新考慮所需的參數網格。
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