In this post, you discovered how to train a final machine learning model for operational use. You have overcome obstacles to finalizing your model, such as:
- Understanding the goal of resampling procedures such as train-test splits and k-fold cross validation.
- Model finalization as training a new model on all available data.
- Separating the concern of estimating performance from finalizing the model.
Source for picture: click here (K-fold cross-validation)
The machine learning model that we use to make predictions on new data is called the final model. There can be confusion in applied machine learning about how to train a final model. This error is seen with beginners to the field who ask questions such as:
- How do I predict with cross validation?
- Which model do I choose from cross-validation?
- Do I use the model after preparing it on the training dataset?
This post will clear up the confusion. It contains the following sections:
- What is a Final Model?
- The Purpose of Train/Test Sets
- The Purpose of k-fold Cross Validation
- Why do we use Resampling Methods?
- How to Finalize a Model?
There is a Q&A section at the bottom, answering the following questions:
- Why not keep the model trained on the training dataset?
- Why not keep the best model from the cross-validation?
- Won’t the performance of the model trained on all of the data be different?
- Each time I train the model, I get a different performance score; should I pick the model with the best score?
The read the full article, click here.
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