Hello, 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. For example, the logistic regression
algorithm uses a regularization rate, hyperparameter to counteract overfitting. And deep learning techniques for
convolutional neural networks or CNN's use hyperparameters like learning
rate to control how weights are adjusted during training. And batch size to determine how many data
items are included in each training batch. It is important to note that machine
learning is an academic field with its own particular terminology. Data scientists refer to the values
determined from the training features as parameters. So a different term is required for
values that are used to configure training behavior but which
are not derived from the training data. Hence the term hyperparameter. The choice of hyperparameter values can
significantly affect the resulting model. Making it important to select
the best possible values for your particular data and
predictive performance goals. Hyperparameter tuning is accomplished
by training the multiple models using the same algorithm and training data but
different hyper parameter 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. In Azure machine learning you achieve this
through an experiment that consists of a hyperdrive run which
initiates a child run for each hyperparameter
combination to be tested. Each of these child runs uses
a training script with parameter arised hyperparameter values to train a model and logs the target performance metric
achieved by the trained model. In this lesson, you will learn how to
tune hyperparameters with Azure machine learning or, more specifically,
define a hyper parameter search space. Configure hyperparameter sampling,
select an early termination policy and run a hyper parameter tuning experiment.