Hyperparameter-Tuning

This is a repository for the medium article published under ADG VIT on Hyper parameters tuning.

Hyperparameters are configuration variables that govern the training process itself. For example, part of setting up a deep neural network is deciding how many hidden layers of nodes to use between the input layer and the output layer, and how many nodes each layer should use. These variables are not directly related to the training data.

This repo consists of

Dataset

The dataset used is Breast Cancer Wisconsin (Diagnostic) Data Set.

The full dataset can be accessed from HERE.

Ten real-valued features are computed for each cell nucleus:

Class distribution:

More Information

Where the model parameters specify how to transform the input data into the desired output, the hyperparameters define how our model is actually structured.

But unfortunately, there’s no one universal way to calculate “which way should one update the hyperparameters to reduce the loss and increase the efficiency?” (For e.g. The learning rate(α) of gradient descent, a method typically used in linear regression algorithm) in order to find the optimal model architecture. Thus, we generally resort to experimentation to figure out what works the best.

The tuning methods used here are:

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