小编ser*_*rez的帖子

import csv
import pandas as pd
import numpy as np
from matplotlib import pyplot 
import shap
from sklearn import preprocessing
from sklearn.preprocessing import StandardScaler
df1=pd.read_csv("./wine.data",sep=",",encoding='utf_8_sig')
X_train = df1
le = preprocessing.LabelEncoder()
X_train['alc_class'] = le.fit_transform(X_train.alc_class.values)
print(X_train.columns)

print(X_train.describe())


y = X_train['alc_class']
X = X_train.drop(columns='alc_class')
import xgboost as xgb


# split X and y into training and testing sets

from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.30, random_state = 2100, stratify = …