SVC 无法找到属性“_probA”
SVC Unable to find the attributes '_probA'
我正在开发机器学习中的信用卡欺诈检测项目。我已经下载了代码形式 GitHub (https://github.com/devrajkataria/Credit-Card-Fraud-Detection-ML-WebApp) 但我收到以下错误：
AttributeError: 'SVC' object has no attribute '_probA'
我尝试更新软件包但没有成功。我使用的是 python 版本的 3.6。这是 code.py
import numpy as np
import sklearn
from flask import Flask, request, jsonify, render_template
import pickle
from sklearn.svm import SVC
app = Flask(__name__)
# prediction function
def ValuePredictor(to_predict_list):
to_predict = np.array(to_predict_list).reshape(1, 7)
loaded_model = pickle.load(open("model.pkl", "rb"))
result = loaded_model.predict(to_predict)
return result[0]
@app.route('/')
def home():
return render_template("index.html")
@app.route('/predict',methods=['POST','GET'])
def predict():
if request.method == 'POST':
to_predict_list = request.form.to_dict()
to_predict_list = list(to_predict_list.values())
to_predict_list = list(map(float, to_predict_list))
result = ValuePredictor(to_predict_list)
if int(result)== 1:
prediction ='Given transaction is fradulent'
else:
prediction ='Given transaction is NOT fradulent'
return render_template("result.html", prediction = prediction)
if __name__ == "__main__":
app.run(debug=True)
这是分类器代码。
# -*- coding: utf-8 -*-
"""Project_Final.ipynb
Automatically generated by Colaboratory.
# Data Preprocessing and Visualisation
"""
# Commented out IPython magic to ensure Python compatibility.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# %matplotlib inline
# Commented out IPython magic to ensure Python compatibility.
import sklearn
import random
from sklearn.utils import shuffle
# %matplotlib inline
from zipfile import ZipFile
with ZipFile('creditcardfraud.zip','r') as zip:
zip.printdir()
zip.extractall()
d=pd.read_csv('creditcard.csv')
sns.distplot(data['Amount'])
sns.distplot(data['Time'])
data.hist(figsize=(20,20))
plt.show()
sns.jointplot(x= 'Time', y= 'Amount', data= d)
class0 = d[d['Class']==0]
len(class0)
class1 = d[d['Class']==1]
len(class1)
class0
temp = shuffle(class0)
d1 = temp.iloc[:2000,:]
d1
frames = [d1, class1]
df_temp = pd.concat(frames)
df_temp.info()
df= shuffle(df_temp)
df.to_csv('creditcardsampling.csv')
sns.countplot('Class', data=df)
"""# SMOTE"""
!pip install --user imblearn
import imblearn
from imblearn.over_sampling import SMOTE
oversample=SMOTE()
X=df.iloc[ : ,:-1]
Y=df.iloc[: , -1]
X,Y=oversample.fit_resample(X,Y)
X=pd.DataFrame(X)
X.shape
Y=pd.DataFrame(Y)
Y.head()
names=['Time','V1','V2','V3','V4','V5','V6','V7','V8','V9','V10','V11','V12','V13','V14','V15','V16','V17','V18','V19','V20','V21','V22','V23','V24','V25','V26','V27','V28','Amount','Class']
data=pd.concat([X,Y],axis=1)
d=data.values
data=pd.DataFrame(d,columns=names)
sns.countplot('Class', data=data)
data.describe()
data.info()
plt.figure(figsize=(12,10))
sns.heatmap(data.corr())
!pip install --user lightgbm
!pip install --user utils
import math
import sklearn.preprocessing
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score , classification_report, confusion_matrix, precision_recall_curve, f1_score, auc
X_train, X_test, y_train, y_test = train_test_split(data.drop('Class', axis=1), data['Class'], test_size=0.3, random_state=42)
"""# Feature Scaling"""
cols= ['V22', 'V24', 'V25', 'V26', 'V27', 'V28']
scaler = StandardScaler()
frames= ['Time', 'Amount']
x= data[frames]
d_temp = data.drop(frames, axis=1)
temp_col=scaler.fit_transform(x)
scaled_col = pd.DataFrame(temp_col, columns=frames)
scaled_col.head()
d_scaled = pd.concat([scaled_col, d_temp], axis =1)
d_scaled.head()
y = data['Class']
d_scaled.head()
"""# Dimensionality Reduction"""
from sklearn.decomposition import PCA
pca = PCA(n_components=7)
X_temp_reduced = pca.fit_transform(d_scaled)
pca.explained_variance_ratio_
pca.explained_variance_
names=['Time','Amount','Transaction Method','Transaction Id','Location','Type of Card','Bank']
X_reduced= pd.DataFrame(X_temp_reduced,columns=names)
X_reduced.head()
Y=d_scaled['Class']
new_data=pd.concat([X_reduced,Y],axis=1)
new_data.head()
new_data.shape
new_data.to_csv('finaldata.csv')
X_train, X_test, y_train, y_test= train_test_split(X_reduced, d_scaled['Class'], test_size = 0.30, random_state = 42)
X_train.shape, X_test.shape
"""# Logistic Regression"""
from sklearn.linear_model import LogisticRegression
lr=LogisticRegression()
lr.fit(X_train,y_train)
y_pred_lr=lr.predict(X_test)
y_pred_lr
from sklearn.metrics import classification_report,confusion_matrix
print(confusion_matrix(y_test,y_pred_lr))
#Hyperparamter tuning
from sklearn.model_selection import GridSearchCV
lr_model = LogisticRegression()
lr_params = {'penalty': ['l1', 'l2'],'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}
grid_lr= GridSearchCV(lr_model, param_grid = lr_params)
grid_lr.fit(X_train, y_train)
grid_lr.best_params_
y_pred_lr3=grid_lr.predict(X_test)
print(classification_report(y_test,y_pred_lr3))
"""# Support Vector Machine"""
from sklearn.svm import SVC
svc=SVC(kernel='rbf')
svc.fit(X_train,y_train)
y_pred_svc=svc.predict(X_test)
y_pred_svc
print(classification_report(y_test,y_pred_svc))
print(confusion_matrix(y_test,y_pred_svc))
from sklearn.model_selection import GridSearchCV
parameters = [ {'C': [1, 10, 100, 1000], 'kernel': ['rbf'], 'gamma': [0.1, 1, 0.01, 0.0001 ,0.001]}]
grid_search = GridSearchCV(estimator = svc,
param_grid = parameters,
scoring = 'accuracy',
n_jobs = -1)
grid_search = grid_search.fit(X_train, y_train)
best_accuracy = grid_search.best_score_
best_parameters = grid_search.best_params_
print("Best Accuracy: {:.2f} %".format(best_accuracy*100))
print("Best Parameters:", best_parameters)
svc_param=SVC(kernel='rbf',gamma=0.01,C=100)
svc_param.fit(X_train,y_train)
y_pred_svc2=svc_param.predict(X_test)
print(classification_report(y_test,y_pred_svc2))
"""# Decision Tree"""
from sklearn.tree import DecisionTreeClassifier
dtree=DecisionTreeClassifier()
dtree.fit(X_train,y_train)
y_pred_dtree=dtree.predict(X_test)
print(classification_report(y_test,y_pred_dtree))
型号代码...
# Commented out IPython magic to ensure Python compatibility.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# %matplotlib inline
# Commented out IPython magic to ensure Python compatibility.
import sklearn
import random
from sklearn.utils import shuffle
# %matplotlib inline
data=pd.read_csv('creditcard.csv')
sns.distplot(data['Amount'])
sns.distplot(data['Time'])
data.hist(figsize=(20,20))
plt.show()
sns.jointplot(x= 'Time', y= 'Amount', data= d)
d=data
class0 = d[d['Class']==0]
len(class0)
class1 = d[d['Class']==1]
len(class1)
class0
temp = shuffle(class0)
d1 = temp.iloc[:2000,:]
d1
frames = [d1, class1]
df_temp = pd.concat(frames)
df_temp.info()
df= shuffle(df_temp)
df.to_csv('creditcardsampling.csv')
sns.countplot('Class', data=df)
"""# SMOTE"""
#!pip install --user imblearn
import imblearn
from imblearn.over_sampling import SMOTE
oversample=SMOTE()
X=df.iloc[ : ,:-1]
Y=df.iloc[: , -1]
X,Y=oversample.fit_resample(X,Y)
X=pd.DataFrame(X)
X.shape
Y=pd.DataFrame(Y)
Y.head()
names=['Time','V1','V2','V3','V4','V5','V6','V7','V8','V9','V10','V11','V12','V13','V14','V15','V16','V17','V18','V19','V20','V21','V22','V23','V24','V25','V26','V27','V28','Amount','Class']
data=pd.concat([X,Y],axis=1)
d=data.values
data=pd.DataFrame(d,columns=names)
sns.countplot('Class', data=data)
data.describe()
data.info()
plt.figure(figsize=(12,10))
sns.heatmap(data.corr())
import math
import sklearn.preprocessing
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score , classification_report, confusion_matrix, precision_recall_curve, f1_score, auc
X_train, X_test, y_train, y_test = train_test_split(data.drop('Class', axis=1), data['Class'], test_size=0.3, random_state=42)
"""# Feature Scaling"""
cols= ['V22', 'V24', 'V25', 'V26', 'V27', 'V28']
scaler = StandardScaler()
frames= ['Time', 'Amount']
x= data[frames]
d_temp = data.drop(frames, axis=1)
temp_col=scaler.fit_transform(x)
scaled_col = pd.DataFrame(temp_col, columns=frames)
scaled_col.head()
d_scaled = pd.concat([scaled_col, d_temp], axis =1)
d_scaled.head()
y = data['Class']
d_scaled.head()
"""# Dimensionality Reduction"""
from sklearn.decomposition import PCA
pca = PCA(n_components=7)
X_temp_reduced = pca.fit_transform(d_scaled)
pca.explained_variance_ratio_
pca.explained_variance_
names=['Time','Amount','Transaction Method','Transaction Id','Location','Type of Card','Bank']
X_reduced= pd.DataFrame(X_temp_reduced,columns=names)
X_reduced.head()
Y=d_scaled['Class']
new_data=pd.concat([X_reduced,Y],axis=1)
new_data.head()
new_data.shape
new_data.to_csv('finaldata.csv')
X_train, X_test, y_train, y_test= train_test_split(X_reduced, d_scaled['Class'], test_size = 0.30, random_state = 42)
X_train.shape, X_test.shape
from sklearn.metrics import classification_report,confusion_matrix
"""# Support Vector Machine"""
from sklearn.svm import SVC
svc=SVC(kernel='rbf',probability=True)
svc.fit(X_train,y_train)
y_pred_svc=svc.predict(X_test)
y_pred_svc
type(X_test)
X_test.to_csv('testing.csv')
from sklearn.model_selection import GridSearchCV
parameters = [ {'C': [1, 10, 100, 1000], 'kernel': ['rbf'], 'gamma': [0.1, 1, 0.01, 0.0001 ,0.001]}]
grid_search = GridSearchCV(estimator = svc,
param_grid = parameters,
scoring = 'accuracy',
n_jobs = -1)
grid_search = grid_search.fit(X_train, y_train)
best_accuracy = grid_search.best_score_
best_parameters = grid_search.best_params_
print("Best Accuracy: {:.2f} %".format(best_accuracy*100))
print("Best Parameters:", best_parameters)
svc_param=SVC(kernel='rbf',gamma=0.01,C=100,probability=True)
svc_param.fit(X_train,y_train)
import pickle
# Saving model to disk
pickle.dump(svc_param, open('model.pkl','wb'))
model=pickle.load(open('model.pkl','rb'))
这是项目结构。
这是我 运行 将项目放入浏览器时的屏幕截图。
这很可能是版本问题。您的 scikit-learn 版本可能是最新版本，您从您提到的 repo 下载的 model.pkl 是旧的且不兼容的版本。
为避免此类问题，我们应该坚持最佳实践，例如使用 requirements.txt 文件准确定义开发期间使用的版本。然后可以在生产环境中安装相同的版本。
如果您确实需要使用存储库中的 model.pkl，我建议创建一个 GitHub 问题，要求 requirement.txt 文件。或者，您可以尝试不同的 scikit-learn 版本，直到它开始工作，但这可能非常麻烦。
我正在开发机器学习中的信用卡欺诈检测项目。我已经下载了代码形式 GitHub (https://github.com/devrajkataria/Credit-Card-Fraud-Detection-ML-WebApp) 但我收到以下错误：
AttributeError: 'SVC' object has no attribute '_probA'
我尝试更新软件包但没有成功。我使用的是 python 版本的 3.6。这是 code.py
import numpy as np
import sklearn
from flask import Flask, request, jsonify, render_template
import pickle
from sklearn.svm import SVC
app = Flask(__name__)
# prediction function
def ValuePredictor(to_predict_list):
to_predict = np.array(to_predict_list).reshape(1, 7)
loaded_model = pickle.load(open("model.pkl", "rb"))
result = loaded_model.predict(to_predict)
return result[0]
@app.route('/')
def home():
return render_template("index.html")
@app.route('/predict',methods=['POST','GET'])
def predict():
if request.method == 'POST':
to_predict_list = request.form.to_dict()
to_predict_list = list(to_predict_list.values())
to_predict_list = list(map(float, to_predict_list))
result = ValuePredictor(to_predict_list)
if int(result)== 1:
prediction ='Given transaction is fradulent'
else:
prediction ='Given transaction is NOT fradulent'
return render_template("result.html", prediction = prediction)
if __name__ == "__main__":
app.run(debug=True)
这是分类器代码。
# -*- coding: utf-8 -*-
"""Project_Final.ipynb
Automatically generated by Colaboratory.
# Data Preprocessing and Visualisation
"""
# Commented out IPython magic to ensure Python compatibility.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# %matplotlib inline
# Commented out IPython magic to ensure Python compatibility.
import sklearn
import random
from sklearn.utils import shuffle
# %matplotlib inline
from zipfile import ZipFile
with ZipFile('creditcardfraud.zip','r') as zip:
zip.printdir()
zip.extractall()
d=pd.read_csv('creditcard.csv')
sns.distplot(data['Amount'])
sns.distplot(data['Time'])
data.hist(figsize=(20,20))
plt.show()
sns.jointplot(x= 'Time', y= 'Amount', data= d)
class0 = d[d['Class']==0]
len(class0)
class1 = d[d['Class']==1]
len(class1)
class0
temp = shuffle(class0)
d1 = temp.iloc[:2000,:]
d1
frames = [d1, class1]
df_temp = pd.concat(frames)
df_temp.info()
df= shuffle(df_temp)
df.to_csv('creditcardsampling.csv')
sns.countplot('Class', data=df)
"""# SMOTE"""
!pip install --user imblearn
import imblearn
from imblearn.over_sampling import SMOTE
oversample=SMOTE()
X=df.iloc[ : ,:-1]
Y=df.iloc[: , -1]
X,Y=oversample.fit_resample(X,Y)
X=pd.DataFrame(X)
X.shape
Y=pd.DataFrame(Y)
Y.head()
names=['Time','V1','V2','V3','V4','V5','V6','V7','V8','V9','V10','V11','V12','V13','V14','V15','V16','V17','V18','V19','V20','V21','V22','V23','V24','V25','V26','V27','V28','Amount','Class']
data=pd.concat([X,Y],axis=1)
d=data.values
data=pd.DataFrame(d,columns=names)
sns.countplot('Class', data=data)
data.describe()
data.info()
plt.figure(figsize=(12,10))
sns.heatmap(data.corr())
!pip install --user lightgbm
!pip install --user utils
import math
import sklearn.preprocessing
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score , classification_report, confusion_matrix, precision_recall_curve, f1_score, auc
X_train, X_test, y_train, y_test = train_test_split(data.drop('Class', axis=1), data['Class'], test_size=0.3, random_state=42)
"""# Feature Scaling"""
cols= ['V22', 'V24', 'V25', 'V26', 'V27', 'V28']
scaler = StandardScaler()
frames= ['Time', 'Amount']
x= data[frames]
d_temp = data.drop(frames, axis=1)
temp_col=scaler.fit_transform(x)
scaled_col = pd.DataFrame(temp_col, columns=frames)
scaled_col.head()
d_scaled = pd.concat([scaled_col, d_temp], axis =1)
d_scaled.head()
y = data['Class']
d_scaled.head()
"""# Dimensionality Reduction"""
from sklearn.decomposition import PCA
pca = PCA(n_components=7)
X_temp_reduced = pca.fit_transform(d_scaled)
pca.explained_variance_ratio_
pca.explained_variance_
names=['Time','Amount','Transaction Method','Transaction Id','Location','Type of Card','Bank']
X_reduced= pd.DataFrame(X_temp_reduced,columns=names)
X_reduced.head()
Y=d_scaled['Class']
new_data=pd.concat([X_reduced,Y],axis=1)
new_data.head()
new_data.shape
new_data.to_csv('finaldata.csv')
X_train, X_test, y_train, y_test= train_test_split(X_reduced, d_scaled['Class'], test_size = 0.30, random_state = 42)
X_train.shape, X_test.shape
"""# Logistic Regression"""
from sklearn.linear_model import LogisticRegression
lr=LogisticRegression()
lr.fit(X_train,y_train)
y_pred_lr=lr.predict(X_test)
y_pred_lr
from sklearn.metrics import classification_report,confusion_matrix
print(confusion_matrix(y_test,y_pred_lr))
#Hyperparamter tuning
from sklearn.model_selection import GridSearchCV
lr_model = LogisticRegression()
lr_params = {'penalty': ['l1', 'l2'],'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}
grid_lr= GridSearchCV(lr_model, param_grid = lr_params)
grid_lr.fit(X_train, y_train)
grid_lr.best_params_
y_pred_lr3=grid_lr.predict(X_test)
print(classification_report(y_test,y_pred_lr3))
"""# Support Vector Machine"""
from sklearn.svm import SVC
svc=SVC(kernel='rbf')
svc.fit(X_train,y_train)
y_pred_svc=svc.predict(X_test)
y_pred_svc
print(classification_report(y_test,y_pred_svc))
print(confusion_matrix(y_test,y_pred_svc))
from sklearn.model_selection import GridSearchCV
parameters = [ {'C': [1, 10, 100, 1000], 'kernel': ['rbf'], 'gamma': [0.1, 1, 0.01, 0.0001 ,0.001]}]
grid_search = GridSearchCV(estimator = svc,
param_grid = parameters,
scoring = 'accuracy',
n_jobs = -1)
grid_search = grid_search.fit(X_train, y_train)
best_accuracy = grid_search.best_score_
best_parameters = grid_search.best_params_
print("Best Accuracy: {:.2f} %".format(best_accuracy*100))
print("Best Parameters:", best_parameters)
svc_param=SVC(kernel='rbf',gamma=0.01,C=100)
svc_param.fit(X_train,y_train)
y_pred_svc2=svc_param.predict(X_test)
print(classification_report(y_test,y_pred_svc2))
"""# Decision Tree"""
from sklearn.tree import DecisionTreeClassifier
dtree=DecisionTreeClassifier()
dtree.fit(X_train,y_train)
y_pred_dtree=dtree.predict(X_test)
print(classification_report(y_test,y_pred_dtree))
型号代码...
# Commented out IPython magic to ensure Python compatibility.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# %matplotlib inline
# Commented out IPython magic to ensure Python compatibility.
import sklearn
import random
from sklearn.utils import shuffle
# %matplotlib inline
data=pd.read_csv('creditcard.csv')
sns.distplot(data['Amount'])
sns.distplot(data['Time'])
data.hist(figsize=(20,20))
plt.show()
sns.jointplot(x= 'Time', y= 'Amount', data= d)
d=data
class0 = d[d['Class']==0]
len(class0)
class1 = d[d['Class']==1]
len(class1)
class0
temp = shuffle(class0)
d1 = temp.iloc[:2000,:]
d1
frames = [d1, class1]
df_temp = pd.concat(frames)
df_temp.info()
df= shuffle(df_temp)
df.to_csv('creditcardsampling.csv')
sns.countplot('Class', data=df)
"""# SMOTE"""
#!pip install --user imblearn
import imblearn
from imblearn.over_sampling import SMOTE
oversample=SMOTE()
X=df.iloc[ : ,:-1]
Y=df.iloc[: , -1]
X,Y=oversample.fit_resample(X,Y)
X=pd.DataFrame(X)
X.shape
Y=pd.DataFrame(Y)
Y.head()
names=['Time','V1','V2','V3','V4','V5','V6','V7','V8','V9','V10','V11','V12','V13','V14','V15','V16','V17','V18','V19','V20','V21','V22','V23','V24','V25','V26','V27','V28','Amount','Class']
data=pd.concat([X,Y],axis=1)
d=data.values
data=pd.DataFrame(d,columns=names)
sns.countplot('Class', data=data)
data.describe()
data.info()
plt.figure(figsize=(12,10))
sns.heatmap(data.corr())
import math
import sklearn.preprocessing
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score , classification_report, confusion_matrix, precision_recall_curve, f1_score, auc
X_train, X_test, y_train, y_test = train_test_split(data.drop('Class', axis=1), data['Class'], test_size=0.3, random_state=42)
"""# Feature Scaling"""
cols= ['V22', 'V24', 'V25', 'V26', 'V27', 'V28']
scaler = StandardScaler()
frames= ['Time', 'Amount']
x= data[frames]
d_temp = data.drop(frames, axis=1)
temp_col=scaler.fit_transform(x)
scaled_col = pd.DataFrame(temp_col, columns=frames)
scaled_col.head()
d_scaled = pd.concat([scaled_col, d_temp], axis =1)
d_scaled.head()
y = data['Class']
d_scaled.head()
"""# Dimensionality Reduction"""
from sklearn.decomposition import PCA
pca = PCA(n_components=7)
X_temp_reduced = pca.fit_transform(d_scaled)
pca.explained_variance_ratio_
pca.explained_variance_
names=['Time','Amount','Transaction Method','Transaction Id','Location','Type of Card','Bank']
X_reduced= pd.DataFrame(X_temp_reduced,columns=names)
X_reduced.head()
Y=d_scaled['Class']
new_data=pd.concat([X_reduced,Y],axis=1)
new_data.head()
new_data.shape
new_data.to_csv('finaldata.csv')
X_train, X_test, y_train, y_test= train_test_split(X_reduced, d_scaled['Class'], test_size = 0.30, random_state = 42)
X_train.shape, X_test.shape
from sklearn.metrics import classification_report,confusion_matrix
"""# Support Vector Machine"""
from sklearn.svm import SVC
svc=SVC(kernel='rbf',probability=True)
svc.fit(X_train,y_train)
y_pred_svc=svc.predict(X_test)
y_pred_svc
type(X_test)
X_test.to_csv('testing.csv')
from sklearn.model_selection import GridSearchCV
parameters = [ {'C': [1, 10, 100, 1000], 'kernel': ['rbf'], 'gamma': [0.1, 1, 0.01, 0.0001 ,0.001]}]
grid_search = GridSearchCV(estimator = svc,
param_grid = parameters,
scoring = 'accuracy',
n_jobs = -1)
grid_search = grid_search.fit(X_train, y_train)
best_accuracy = grid_search.best_score_
best_parameters = grid_search.best_params_
print("Best Accuracy: {:.2f} %".format(best_accuracy*100))
print("Best Parameters:", best_parameters)
svc_param=SVC(kernel='rbf',gamma=0.01,C=100,probability=True)
svc_param.fit(X_train,y_train)
import pickle
# Saving model to disk
pickle.dump(svc_param, open('model.pkl','wb'))
model=pickle.load(open('model.pkl','rb'))
这是项目结构。
这是我 运行 将项目放入浏览器时的屏幕截图。
这很可能是版本问题。您的 scikit-learn 版本可能是最新版本，您从您提到的 repo 下载的 model.pkl 是旧的且不兼容的版本。
为避免此类问题，我们应该坚持最佳实践，例如使用 requirements.txt 文件准确定义开发期间使用的版本。然后可以在生产环境中安装相同的版本。
如果您确实需要使用存储库中的 model.pkl，我建议创建一个 GitHub 问题，要求 requirement.txt 文件。或者，您可以尝试不同的 scikit-learn 版本，直到它开始工作，但这可能非常麻烦。