- [Instructor] In this video,
we're going to look at how
to import a Machine Learning experiment from the gallery.
And we'll discuss how to optimize your workflow
to improve accuracy.
So we're here in the Azure Machine Learning Studio.
Now let's import an experiment.
I'm going to click on the new button
on the bottom-left of our screen.
And I know I wanna find an experiment
that deals with demand.
So I'm going to search for demand.
And we get two responses,
one of them is regression demand estimation,
and let's go ahead and click on open in Studio.
Now as you can see here on the canvas,
the steps involved can get quite complex.
So what are we lookin' at here?
Well, first of all, I know that our sample data is intended
to help us to predict the demand
of bike rentals based on certain market conditions.
Let's have a look at the data.
So one of the things that you can do here
on the canvas, is that you can zoom in on your model.
So I'm going to select the plus icon.
And I'm then going to hold down my shift bar
so I can scroll the model up.
And then I'm going to right-click
on the bike rental UCI data set module.
And from there, I can click on data set and visualize,
and that will show us what we're working with.
And so it appears as if we have roughly 17,000 records
running over 17 different columns.
And so it looks like we have a good bit
of information related to the time of day
and the particular day and weather overall.
What we're working to do is to predict this variable,
the cnt variable,
which is the count of bike rentals by the hour.
Let's go ahead and close out of this.
Let's use this little icon here, zoom-to-fit,
to zoom back out.
Now in data science,
we use a term called predictive variables.
These are the conditions that can help predict some outcome.
In machine learning, that same concept is known as features.
The right set of features can help increase the accuracy
of our machine-learning service.
Now in this example,
what you are seeing are really four different sets
of features.
Each set is being assessed to determine the best group.
They're all being run against a regression algorithm
because the field we're trying to predict,
that cnt field is a continuous set of numbers.
And while we're on the subject,
one thing that's good to be aware of is
that there are four different algorithm types
in the Machine Learning Studio.
You can see those on the left-hand side.
If I collapse save data sets,
then some of these other options here,
under the Machine Learning heading,
and under initialize model,
we have anomaly detection,
classification, clustering,
and regression.
So in this demonstration, you were introduced
to how to import an experiment from the gallery.
And we took a look at how our workflow can be set up
to determine the best set of features.
And we talked about the different types of algorithms
that are part of the Machine Learning Studio.
This is an approach you can take
to help automate your demand forecasting.