警告出現(xiàn)于在使用sklearn中的MLPClassifier（多層神經(jīng)網(wǎng)絡(luò)分類(lèi)器）中
完整警告信息：

/usr/local/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but MLPClassifier was fitted without feature names
warnings.warn(

警告部分代碼：

from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import StandardScaler

# Scale the data
def scale(x):
    scaler = StandardScaler()  
    scaler.fit(x) 
    x = scaler.transform(x)
    return x

# 3 Multi-layer Perceptron Classifier
def MLPClf(x, y):
    x = scale(x)
    clf = MLPClassifier(solver='sgd', alpha=1e-5, max_iter=400, hidden_layer_sizes=(5,), random_state=1)
    clf = clf.fit(x, y)
    return clf

分析和解決辦法：因?yàn)镸LPClassifier需要包含feature names的輸入變量，但是x由于經(jīng)過(guò)了StandardScaler()的feature scaling，導(dǎo)致其被轉(zhuǎn)化為了array格式，也就不存在feature names了。因此解決方法也很直觀，再把scaling后的x轉(zhuǎn)回dataframe就可以了，唯一要注意的是要提前把columns儲(chǔ)存起來(lái)。
修改后代碼：

from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import StandardScaler

# Scale the data
def scale(x):  
    # store columns in advance
    cols = x.columns
    scaler = StandardScaler()  
    scaler.fit(x) 
    x = scaler.transform(x)
    # avoid warning, transform it back to df
    x = pd.DataFrame(x, columns=cols)
    return x

# 3 Multi-layer Perceptron Classifier
def MLPClf(x, y):
    x = scale(x)
    clf = MLPClassifier(solver='sgd', alpha=1e-5, max_iter=400, hidden_layer_sizes=(5,), random_state=1)
    clf = clf.fit(x, y)
    return clf