The saving and re-use of training model in machine learning-python

In the model training, especially in the training set to do cross-validation, usually want to save the model, and then put on a separate test set test, the following is the Python training model to save and reuse.

Scikit-learn already has the model persisted operation, the import joblib can

fromimport joblib

Model Save

>>>  Os.chdir ( "Workspace/model_save" ) >>>  from  sklearn import  SVM >>>  X = [[0 , 0 ], [1  , 1 ]]>>>  y = [ 0 , 1 ]>>>  CLF = SVM. SVC () >>>  clf.fit (X, y) >>>  Clf.fit (train_x,train_y) >>>  joblib.dump (CLF,  "TRAIN_MODEL.M" )  

By Joblib dump you can save the model locally, CLF is a trained classifier

Model Recall from local

>>> clf = joblib.load("train_model.m")

The saved model is loaded by the Joblib load method.

Then you can test it on the test set.

clf.predit(test_X，test_y)

Copyright NOTICE: This article for Bo Master original article, without Bo Master permission not reproduced.

The saving and re-use of training model in machine learning-python