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• Parameter Tuning
Once you’ve done evaluation, it’s possible that you want to see if you can further improve your training in any way. We can do this by tuning our parameters. There were a few parameters we implicitly assumed when we did our training, and now is a good time to go back and test those assumptions and try other values.
One example is how many times we run through the training dataset during training. What I mean by that is we can “show” the model our full dataset multiple times, rather than just once. This can sometimes lead to higher accuracies.
Another parameter is “learning rate”. This defines how far we shift the line during each step, based on the information from the previous training step. These values all play a role in how accurate our model can become, and how long the training takes.
For more complex models, initial conditions can play a significant role in determining the outcome of training. Differences can be seen depending on whether a model starts off training with values initialized to zeroes versus some distribution of values, which leads to the question of which distribution to use.
As we can see there are many considerations at this phase of training, and it’s important that we define what makes a model “good enough”, otherwise we might find our self-tweaking parameters for a very long time. These parameters are typically referred to as “hyperparameters”. The adjustment, or tuning, of these hyperparameters, remains a bit of an art, and is more of an experimental process that heavily depends on the specifics of your dataset, model, and training process.
Once we’re happy with our training and hyperparameters, guided by the evaluation step, it’s time to finally use our model to do something useful!
Machine learning is using data to answer questions. So Prediction, or inference, is the step where we get to answer some questions. This is the point of all this work, where the value of machine learning is realized.