Hyperparameter tuning
Last updated
Last updated
Model parameters are internal to the model whose values can be estimated from the data and we are often trying to estimate them as best as possible.
Whereas hyperparameters are external to our model and cannot be directly learned from the regular training process. These parameters express “higher-level” properties of the model such as its complexity or how fast it should learn. Hyperparameters are model-specific properties that are ‘fixed’ before you even train and test your model on data.
It takes a dictionary of all of the different hyperparameters that you want to test, and then feeds all of the different combinations through the algorithm for you and then reports back to you which one had the highest accuracy. The disadvantage of this method is its high computational cost.
To avoid going through all the possible combinations, this method evaluates n uniformly random points in the hyperparameter space, and selects the one producing the best performance.
Well, this is adjusting the hyperparameters manually based on experience or deep knowledge of the data and the picked estimator.
