Hyperparameter Tuning In
December 15, 2020 by Meenal Sarda Leave a Comment
In 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.
In this blog, we are going to cover the basics of hyperparameters,
hyperparameter tuning, search space, and how to tune hyperparameters in
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.
Note: Do Checkout Our Blog Post On ML Operations.
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.
Note: Do Read our Blog on Convolution Neural Network.
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What Is Hyperparameter
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