clf2 = pickle.load(f) #测试读取后的Model print(clf2.predict(X[0:1]))# [0] # 方法二:joblib. joblib是sklearn的外部模块。 from sklearn.externals import joblib #jbolib模块 #保存Model(注:save文件夹要预先建立,否则会报错) joblib.dump(clf, 'save_model/clf.pkl') #读取Model clf3 = joblib...
with open('save/clf.pickle', 'wb') as f: pickle.dump(clf, f) #读取Model with open('save/clf.pickle', 'rb') as f: clf2 = pickle.load(f) #测试读取后的Model print(clf2.predict(X[0:1])) === 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 使用joblib 保存 joblib...
loaded_vec = CountVectorizer(decode_error = "replace", vocabulary = pickle.load(open(feature_path, "rb"))) return clf, loaded_vec if __name__ =="__main__": path = "../rg_train_20171230_1000008.txt" # 保存模型 # saveModel(path) # 加载模型 global vectorizer clf, vectorizer = use...
model.fit(X_train, y_train) # 使用 pickle 保存模型 with open('./random_forest_model.pkl', 'wb') as file: pickle.dump(model, file) 模型保存为pkl文件: 2.3 模型推理与评价 加载训练好的模型(文件),输入测试集进行预测: # 加载保存的模型withopen('./random_forest_model.pkl','rb')asfile:l...
('save/clf.pickle','rb')asf:clf2=pickle.load(f)#测试读取后的Modelprint(clf2.predict(X[0:1]))#使用 joblib 保存fromsklearn.externalsimportjoblib#jbolib模块#保存Model(注:save文件夹要预先建立,否则会报错)joblib.dump(clf,'save/clf.pkl')#读取Modelclf3=joblib.load('save/clf.pkl')#测试读取...
在下面的几行代码中,我们会把上面得到的模型保存到pickle_model.pkl文件中,然后将其载入。最后,使用载入的模型基于测试数据计算 Accuracy,并输出预测结果。 代码语言:javascript 复制 importpickle # # Create your modelhere(sameasabove)# # Save to fileinthe current working directory ...
第一个方法就是使用pickle,将模型进行序列化 和 反序列化。 It is possible to save a model in scikit-learn by using Python’s built-in persistence model, namelypickle: >>>fromsklearnimportsvm>>>fromsklearnimportdatasets>>> clf =svm.SVC()>>> X, y= datasets.load_iris(return_X_y=True)>...
model.fit(X_train, Y_train) # save the model to disk filename = 'finalized_model.sav' pickle.dump(model, open(filename, 'wb')) # load the model from disk loaded_model = pickle.load(open(filename, 'rb')) result = loaded_model.score(X_test, Y_test)Copyright...
model.coef_ :输出模型的斜率 model.intercept_ :输出模型的截距(与y轴的交点) model.get_params() :取出之前定义的参数 model.score(data_X, data_y) :对 Model 用 R^2 的方式进行打分 训练和预测: from sklearn import datasetsfrom sklearn.linear_model import LinearRegression loaded_data = datasets....
print(model.get_params())# 获取模型的参数设置 {'copy_X':True,'fit_intercept':True,'n_jobs':None,'normalize':False,'positive':False} # 模型的评价,这里线性回归是R^2,即SSR/SST(SST=SSE+SSR)print(model.score(data_X, data_Y))