我训练了一个 xgboost 分类器，它给出了 49.99% 的准确率，我将该模型保存到一个 pickle 文件中。当我使用 pickle 文件 (.pkl) 运行相同的数据时，它的准确率为 88.99%。我不知道为什么会这样。请帮助我摆脱这种情况。

bank_dataset = pd.read_csv(r"dataset.csv")

missing_val = pd.DataFrame(bank_dataset.isnull().sum())

bank_dataset[' Balance'] = bank_dataset[' Balance'].fillna(bank_dataset[' Balance'].mean())

from sklearn.preprocessing import LabelEncoder

le = LabelEncoder()

objList = bank_dataset.select_dtypes(include = "object").columns

for feat in objList:
    bank_dataset[feat] = le.fit_transform(bank_dataset[feat].astype(str))

correlation = bank_dataset.corr()
print(correlation['Outcome'].sort_values(ascending = False), '\n')

k = 10
cols = correlation.nlargest(k, 'Outcome')['Outcome'].index
print(cols)
cm = np.corrcoef(bank_dataset[cols].values.T)
f, ax = plt.subplots(figsize=(14,14))
sns.heatmap(cm, vmax = .8, linewidths = 0.01, square = True, annot = True, cmap = "coolwarm", linecolor = "white", 
           annot_kws = {'size':12}, xticklabels = cols.values, yticklabels = cols.values)

X = bank_dataset.iloc[:, [7,8,12,24,11,16,4,18,20]].values
y = bank_dataset.iloc[:, -4].values

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 1)
  
from xgboost import XGBClassifier

xg = XGBClassifier()
xg.fit(X_train, y_train)

y_pred = xg.predict(X_test)

from sklearn.metrics import confusion_matrix
from sklearn import metrics
print(metrics.accuracy_score(y_test, y_pred))
cm = confusion_matrix(y_test, y_pred)

import pickle

filename = 'xg.pkl'
pickle.dump(xg, open(filename, 'wb'))

上面用于训练模型并将模型保存到 .pkl 中的代码

bank_dataset = pd.read_csv(r"dataset.csv")

from sklearn.preprocessing import LabelEncoder

le = LabelEncoder()

objList = bank_dataset.select_dtypes(include = "object").columns

for feat in objList:
    bank_dataset[feat] = le.fit_transform(bank_dataset[feat].astype(str))

X = bank_dataset.iloc[:, [7,8,12,24,11,16,4,18,20]].values
y = bank_dataset.iloc[:, -4].values

file = open('xg.pkl', 'rb')

data = pickle.load(file)

X_ = data.predict(X)

第二个代码是运行带有用于训练的相同数据的 pkl 文件的代码。