Question

我正在尝试构建模型并创建网格搜索，下面是代码。从此站点下载原始数据（信用卡欺诈数据）。 https://www.kaggle.com/mlg-ulb/creditcardfraud

读取数据后从标准化开始的代码。

standardization = StandardScaler()
credit_card_fraud_df[['Amount']] = standardization.fit_transform(credit_card_fraud_df[['Amount']])
# Assigning feature variable to X
X = credit_card_fraud_df.drop(['Class'], axis=1)

# Assigning response variable to y
y = credit_card_fraud_df['Class']
# Splitting the data into train and test
X_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 data
X_train_pt_df = power_transformer.transform(X_train)    ## Then apply on all data
X_test_pt_df = power_transformer.transform(X_test)
y_train_pt_df = y_train
y_test_pt_df = y_test
train_pt_df = pd.DataFrame(data=X_train_pt_df, columns=X_train.columns.tolist())
# set up cross validation scheme
folds = StratifiedKFold(n_splits = 5, shuffle = True, random_state = 4)

# specify range of hyperparameters
params = {"C":np.logspace(-3,3,5,7), "penalty":["l1","l2"]}# l1 lasso l2 ridge

## using Logistic regression for class imbalance
model = LogisticRegression(class_weight='balanced')
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_)
cv_results

抽样结果：

  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

我在输入数据中没有空值。我不明白为什么我要为这些列获取Nan值。谁能帮我吗？

Answer 1

您对此处定义的默认求解器有疑问：

model = LogisticRegression(class_weight='balanced')

，它来自以下错误消息：

ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

此外，在定义参数网格之前研究docs可能会很有用：

惩罚：{'l1'，'l2'，'elasticnet'，'none'}，默认值='l2' 用于指定惩罚中使用的规范。 “ newton-cg”，“ sag”和“ lbfgs”求解器仅支持l2惩罚。 “ saga”求解器仅支持“ elasticnet”。如果为“无”（liblinear求解器不支持），则不应用任何正则化。

只要使用支持所需网格的其他求解器对其进行校正，就可以了：

## 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_)

请注意ConvergenceWarning，这可能表明您需要增加默认值max_iter，tol，或切换到另一个求解器，然后重新考虑所需的参数网格。

使用正则化（L1 / L2）Lasso和Ridge的Logistic回归模型

1 个答案: