Scikit learn ComplementNB 正在为分数输出 NaN

Question

我有一个包含 23 个特征的不平衡二进制数据集，92000 行标记为 0，207,000 行标记为 1。

我在这个数据集上训练了模型，例如 GaussianNB、DecisionTreeClassifier，以及来自 scikit learn 的其他几个分类器，它们都运行良好。

我想在这个数据集上运行 ComplementNB，但是当我这样做时，所有的分数都以 NaN 的形式出现。

下面是我的代码：

from sklearn.naive_bayes import ComplementNB
features = [
            # Chest accelerometer sensor
            'chest_accel_x', 'chest_accel_y', 'chest_accel_z',
    
            # ECG (2 leads)
            'ecg_1', 'ecg_2',

            # Left ankle sensors
            'left_accel_x', 'left_accel_y', 'left_accel_z',
            'left_gyro_x', 'left_gyro_y', 'left_gyro_z',
            'left_mag_x', 'left_mag_y', 'left_mag_z',

            # Right lower arm sensors
            'right_accel_x', 'right_accel_y', 'right_accel_z',
            'right_gyro_x', 'right_gyro_y', 'right_gyro_z',
            'right_mag_x', 'right_mag_y', 'right_mag_z',
        ]
df = pd.read_csv('mhealth_s_m.csv')
X = df[features]
y = df['label']
train_X, test_X, train_y, test_y = train_test_split(X, y, test_size = 0.2, random_state = 69)
def K_fold_unbalanced(train_X, train_y):
        scoring = ['accuracy', 'f1', 'precision', 'recall', 'roc_auc']
        print('Unbalanced Data')
        model = ComplementNB()
        start_time = time.time()
        scores = cross_validate(model, train_X, train_y, scoring = scoring, cv = 5, return_train_score = True)
        print(scores)
        print('Took', time.time() - start_time, 'to run')
        print('=======================================')
K_fold_unbalanced(train_X, train_y)

Output 是：

train accuracy nan 
 train f1 nan 
 train precision nan 
 train recall nan 
 train roc auc nan

test accuracy nan 
 test f1 nan 
 test precision nan 
 test recall nan 
 test roc auc nan
Took 0.12271976470947266 to run

知道为什么所有值都是 NaN 吗？ 我的数据可以在这里找到

Answer 1

这修复了它：

from sklearn.preprocessing import MinMaxScale

scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)