UserWarning：X 没有有效的特征名称，但 LogisticRegression 已安装了特征名称

我在 Flask 中编写了一个程序来获取用户的输入，以输入长度和宽度来预测鱼的类型，但是当我输入时，它会显示一个错误，称为

UserWarning: X does not have valid feature names, but LogisticRegression was fitted 
with feature names

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

df=pd.read_csv('Fish.csv')
df.head()

X = df.drop('Species', axis=1)
y = df['Species']

cols = X.columns
index = X.index

from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=0)

from sklearn.ensemble import RandomForestClassifier
random=RandomForestClassifier()
random.fit(X_train,y_train)
y_pred=random.predict(X_test)

from sklearn.metrics import accuracy_score
score=accuracy_score(y_test,y_pred)

# Create a Pickle file  
import pickle
pickle_out = open("model.pkl","wb")
pickle.dump(logistic_model, pickle_out)
pickle_out.close()

logistic_model.predict([[242.0,23.2,25.4,30.0,11.5200,4.0200]])

import numpy as np
import pickle
import pandas as pd
from flask import Flask, request, jsonify, render_template

app=Flask(__name__)
pickle_in = open("model.pkl","rb")
random = pickle.load(pickle_in)

@app.route('/')
def home():
    return render_template('index.html')


@app.route('/predict',methods=["POST"])
def predict():
    """
    For rendering results on HTML GUI
    """
    int_features = [x for x in request.form.values()]
    final_features = [np.array(int_features)]
    prediction = random.predict(final_features)
    return render_template('index.html', prediction_text = 'The fish belongs to species {}'.format(str(prediction)))

if __name__=='__main__':
    app.run()

数据集https://www.kaggle.com/datasets/aungpyaeap/fish-market https://www.kaggle.com/datasets/aungpyaeap/fish-market

我也面临同样的警告： 用户警告：X 没有有效的特征名称，但 LogisticRegression 已安装了特征名称。

这个警告实际上是在将数据拟合到我们的模型时说的model.fit(), that dataframe X_train具有属性名称，但是当您尝试使用转换为行向量的数据帧或 numpy 数组进行预测时，您没有向要进行预测的元组提供特征/属性名称。

For understanding clearly what i meant to say, just see sample image below:

希望这可以帮助初学者通过模型对看不见的数据进行预测