>> Hi everyone. Welcome back to
developers Introduction to Data Science.
So let's learn together,
what is Model Interpretability?
How we can use
a model interpretability toolkits for
our Machine Learning and Data Science solutions?
Interpretability is critical for data scientists,
but also auditors and business decision-makers
to ensure compliance with the company policies,
industry standards, and government regulations.
As you can see here, at least that three different
categories that I think are very,
very interested in using
model interpretability to explain their Machine Learning models.
These are data scientist because they need the ability
to explain their models to executive,
or to other stakeholders, customers,
so they can understand the value
and the accuracy of their findings.
We have the legal auditors.
They for sure require tools to validate the models with respect to
regulatory compliance and monitor how
models decisions are impacting other people.
Finally, business decision makers,
they need a peace of mind by having
the ability to provide the transparency for end users.
So these allows them to earn and maintain that trust.
In Azure Machine Learning,
we have the different interpretability classes
that you can see here.
For example, we have the azureml.explain.model.
This is the main package that
contains the functionalities as supported by Microsoft.
We have the azureml.contrib.explain.model.
This is a preview, and
these experimental functionalities that you can try.
Finally, we have also the azureml.train.automl.automlexplainer.
This is a package for understanding and
explaining your automated Machine Learning models.
Using these different classes and methods in SDK,
you can explain the model prediction,
you can achieve model interpretability,
which is very important.
You can use an interactive visualization.
Let us see what I mean with the interactive visualizations.
We have two different types of visualization.
We have the global visualizations.
For example, the following plots provide
a non-verbal overall overview of
the train model along with its predictions and explanations.
In terms of plots, you can use them for data exploration,
the global importance, explanation,
exploration, summary, and so on.
Then we have what we call the local visualizations.
You can know that the individual feature importance plot
for any data point by
clicking on any of the individual data points in different plots.
As you can see in terms of a plot,
we have the local importance,
pertubation exploration, individual conditional expectations.
So all of these are
visualizations that allows you to understand better
what your model is doing and why
is predicting a specific type of the results.
As I mentioned, the interpretability is also available for
automated Machine Learning in order to enable
the feature importance for a train ensemble model,
you can use the explain_model function.
In order to enable
the feature importance for each individual run prior to training,
you can set that the model_explainability parameter
to True in your AutoMLConfig object,
along with providing of the validation data.
So this is again very important,
if you want to use a different interpretability toolkit,
with your Machine Learning and automated Machine Learning models.
To learn more, you can go to aka.ms/ModelInterpretability.