What is the method of saving and loading API model
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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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