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This article introduces the relevant knowledge of "what is the method of saving and loading API model". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

1. Objective:

Save the trained model and prepare it for next time to save training time and improve efficiency.

2.API:

From sklearn.externals import joblib

Save:

Joblib.dump (rf, "test.pkl")

Load:

Estimator = joblib.load ("test.pkl")

3.Python code implementation:

#-*-coding: UTF-8-*-

''

@ Author: Jason

Boston house price forecast, save the model to

''

From sklearn.datasets import load_boston

From sklearn.model_selection import train_test_split

From sklearn.preprocessing import StandardScaler

From sklearn.linear_model import Ridge

From sklearn.metrics import mean_squared_error

From sklearn.externals import joblib

Def model_save_fetch ():

""

Ridge regression is used to predict house prices in Boston.

: return:

""

# 1) obtain data

Boston = load_boston ()

Print ("number of features:\ n", boston.data.shape)

# 2) dividing the data set, which is a good http://fk.zyfuke.com/ for Zhengzhou Gynecology Hospital

X_train, x_test, y_train, y_test = train_test_split (boston.data, boston.target, random_state=22)

# 3) Standardization

Transfer = StandardScaler ()

X_train = transfer.fit_transform (x_train)

X_test = transfer.transform (x_test)

# # 4) predictor

# estimator = Ridge (alpha=0.5, max_iter=10000)

# estimator.fit (x_train, y_train)

#

# # Save the model

# joblib.dump (estimator, ". / files/test.pkl")

# load model

Estimator = joblib.load (". / files/test.pkl")

# 5) draw the model

Print ("Ridge regression-weight coefficient is:\ n", estimator.coef_)

Print ("Ridge regression-bias is:\ n", estimator.intercept_)

# 6) Model evaluation

Y_predict = estimator.predict (x_test)

Print ("Forecast House prices:\ n", y_predict)

Error = mean_squared_error (y_test, y_predict)

Print ("Ridge regression-mean square error:\ n", error)

Return None

If _ name__ = = "_ _ main__":

Model_save_fetch ()

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