Hyperparameter

created:: 2023-09-23T21:43:23
up:: Machine Learning
tags:: #🌱 #machine-learning

In the context of Machine Learning, hyperparameter is some kind of knobs that you can turn to left or right - that has impact to the result/output.

Each knob will result to different impact, turning the first knob to right might increase the performance, but when you turn the second knob, it might decrease the performance.

The simple word for hyperparameter is config or settings. One note that hyperparameter is different from Parameter.

It is internally not connected to the machine/model.

Here is an example of the hyperparameter:

# xgb hyperparameter
parameters = {  
	"n_estimators": [10, 50, 100],  
	"subsample":[0.6, 0.8, 1],  
	"learning_rate":[0.01, 0.1, 0.5, 1],  
	"gamma":[0.01, 0.1, 1, 5],  
	"colsample_bytree":[0.5, 0.7, 0.9, 1],  
	"alpha":[0, 0.1, 0.5]  
}

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