How to save and load model with joblib

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How to save and load model with joblib

Saving and loading a model with joblib in Python is a way to preserve the trained model for future use. It allows you to save the model’s parameters, so you can use it later without the need to train the model again. This can save a lot of time and computational resources, especially if the model takes a long time to train or if you want to use the model on new data.

Joblib is a library in Python that allows you to save and load models in a simple and efficient way. It can save the model’s parameters in a binary format, which makes it easier to load the model later.

To save a model with joblib, you first need to import the joblib library, then you can use the dump function to save the model. The dump function takes two arguments: the model and the file name where the model will be saved.

To load a model with joblib, you first need to import the joblib library, then you can use the load function to load the model. The load function takes one argument: the file name of the saved model.

When loading a model, the model object is returned, you can use this object to make predictions on new data.

Another advantage of using joblib is that it can handle large numpy arrays and pickle files, which makes it a suitable choice for saving and loading large data sets and machine learning models.

In summary, Saving and loading a model with joblib in Python is a way to preserve the trained model for future use. Joblib is a library in Python that allows you to save and load models in a simple and efficient way. To save a model with joblib, you use the dump function and to load it, you use the load function. When loading a model, the model object is returned, you can use this object to make predictions on new data. Joblib can handle large numpy arrays and pickle files, which makes it a suitable choice for saving and loading large data sets and machine learning models.

 

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How to save and load model with joblib

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