hyperparameter tuningIn machine learning, models are trained to predict unknown labels for new data based on correlations between known labels and features found in the training data. Depending on the algorithm used, you may need to specify hyperparameters to configure how the model is trained.
What Are Hyperparameters?
There are two types of parameters in machine learning:
Model Parameters are parameters in the model that must be determined using the training data set. These are the fitted parameters. For Eg: eights and biases, or split points in the Decision Tree, and more.
Hyperparameters are adjustable parameters that control the model training process. Model performance depends heavily on hyperparameters.
Selecting good hyperparameters has the following advantage:
- Efficient search across the space of possible hyperparameters
- Easy management of a large set of experiments for hyperparameter tuning.
What Is Hyperparameter Tuning?
Hyperparameter tuning is the process of finding the configuration of hyperparameters that will result in the best performance. The process is computationally expensive and a lot of manual work has to be done. It is accomplished by training the multiple models, using the same algorithm and training data but different hyperparameter values. The resulting model from each training run is then evaluated to determine the performance metric for which you want to optimize (for example, accuracy), and the best-performing model is selected.
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