I'm having a bit of trouble setting up this cross validate module in MS Machine Learning Studio.
I'm not sure which value it's referring to as needing, since the links connected to it just fine, and those were the values I thought I was submitting.
Nor do I think it's referring to the Random Seed, as I've gotten models to work fine without changing that value.
Any tips on how to make that module work?
This is the resource I'd been looking at before coming here:
https://gallery.azure.ai/Experiment/333e0a99ceac457d8992ef83bfbd98b6
An image of the module flow
You need to set the Label column in Cross Validate, i.e. the column that you're trying to train your model to be able to predict. In your case, that's most likely the Price column.
Related
I have a labelled dataset that I haven't exported yet. I can flag each image as verified. But do I need to do this or is it optional? I saw this option sync across several labelling software.
I've mostly labelled the dataset but I am scared that the images may be ignored if I don't self verify them
To anyone confused in the future. No you do not have to!. Here is the definition I just found right after posting.
"The user can flag the image as verified, a green background will appear. This is used when creating a dataset automatically"
I have a principal model that involves a lot of components and I want some connections to change when the pressure value changes.
For example
if Pressure<1 then connect (evap.a,fad.b) else connect (evap.a,gol.c)
Can anyone here help me please, I already tried using if and when and it does not work.
What you want is not possible if Pressure is a variable, only if is a parameter or constant. All the connect equations need to be known at compile time.
I am currently exploring the notion of using iris in a project to read forecast grib2 files using python.
My aim is to load/convert a grib message into an iris cube based on a grib message key having a specific value.
I have experimented with iris-grib, which uses gribapi. Using iris-grib I have not been to find the key in the grib2 file, althrough the key is visible with 'grib_ls -w...' via the cli.
gribapi does the job, but I am not sure how to interface it with iris (which is what, I assume, iris-grib is for).
I was wondering if anyone knew of a way to get a message into an iris cube based on a grib message key having a specific value. Thank you
You can get at anything that the gribapi understands through the low-level grib interface in iris-grib, which is the iris_grib.GribMessage class.
Typically you would use for msg in GribMessage.messages_from_filename(xxx): and then access it like e.g. msg.sections[4]['productDefinitionTemplateNumber']; msg.sections[4]['parameterNumber'] and so on.
You can use this to identify required messages, and then convert to cubes with iris_grib.load_pairs_from_fields().
However, Iris-grib only knows how to translate specific encodings into cubes : it is quite strict about exactly what it recognises, and will fail on anything else. So if your data uses any unrecognised templates or data encodings it will definitely fail to load.
I'm just anticipating that you may have something unusual here, so that might be an issue?
You can possibly check your expected message contents against the translation code at iris_grib:_load_convert.py, starting at the convert() routine.
To get an Iris cube out of something not yet supported, you would either :
(a) extend the translation rules (i.e. a Github PR), or
(b) sometimes you can modify the message so that it looks like something
that can be recognised.
Failing that, you can
(c) simply build an Iris cube yourself from the data found in your GribMessage : That can be a little simpler than using 'gribapi' directly (possibly not, depending on detail).
If you have a problem like that, you should definitely raise it as an issue on the github project (iris-grib issues) + we will try to help.
P.S. as you have registered a Python3 interest, you may want to be aware that the newer "ecCodes" replacement for gribapi should shortly be available, making Python3 support for grib data possible at last.
However, the Python3 version is still in beta and we are presently experiencing some problems with it, now raised with ECMWF, so it is still almost-but-not-quite achievable.
I want to know what is the difference between feature numeric and numeric columns in Azure Machine Learning Studio.
The documentation site states:
Because all columns are initially treated as features, for modules
that perform mathematical operations, you might need to use this
option to prevent numeric columns from being treated as variables.
But nothing more. Not what a feature is, in which modules you need features. Nothing.
I specifically would like to understand if the clear feature dropdown option in the fields in the edit metadata-module has any effect. Can somebody give me a szenario where this clear feature-operation changes the ML outcome? Thank you
According to the documentation in ought to have an effect:
Use the Fields option if you want to change the way that Azure Machine
Learning uses the data in a model.
But what can this effect be? Any example might help
As you suspect, setting a column as feature does have an effect, and it's actually quite important - when training a model, the algorithms will only take into account columns with the feature flag, effectively ignoring the others.
For example, if you have a dataset with columns Feature1, Feature2, and Label and you want to try out just Feature1, you would apply clear feature to the Feature2 column (while making sure that Feature1 has the feature label set, of course).
I'm building a network business model in excel. A similar model is that of Gawker Media.
In my model I have a number properties that have some over lap of audience. Each property attracts users, which in turn affords cross promotional opportunities. In the case of Gawker they have a series of blogs whose audience will likely read several of their blogs in their network.
If gawker launched a new blog they're able to direct traffic from their blog network.
Creating a model for a single blog is fairly simple - although the initial assumptions are harder. The next step is to model the network effect.
Excel provides a scenarios manager that allows me to vary the key assumptions in the basic model. This is almost perfect, I can model the launch of 10 properties, each with different launch assumptions and see the summary.
Where I need help is figuring out how I can vary the initial number of users for the launch of each property. In other words, once the network is established, its possible to drive people to any new property launched on the network.
I don't believe the scenario manager will do what I need.
So, I'm wondering if its possible to use the model work sheet as a UDF? The UDF would need to spit out the monthly revenue and unique users given a number of input assumptions.
I would then be able to create my own summary sheet for the 10 properties and using the total uniques for each property get a summary for the network. This network summary would be used to determine how many people could be driven to the launch of a new property.
In effect, the only difference to the scenario manager is that I need one of my input variables (initial users) to be programmatically generated as a function of the number of people in the network at the time of launch.
I'm hoping its possible to achieve something along these lines in excel. I could drop down and create the whole model in Java, but then its much harder to share with business colleagues!
Thanks - Matt.
You could try Data Table.
It only allows you to analyse the effect of varying 2 input parameters, but you can create several data tables, and each parameter can take hundreds of different values.
It's little know, but efficient and available since Excel 3.0.
There is a product that I have researched but never used - search for calc4web. It takes a sheet of formulas and generates code (C++) that can be compiled into an XLL add-in. Then you can call a function that does what your sheet does. But of course then you have an XLL to distribute, and a build step every time you change your logic, which defeats much of the point of using a spreadsheet.
In my case, I wound up writing some very simple VBA code to vary my sheet "inputs" using the scenario manager, and capture my "outputs". This works if you have a batch of inputs that you can just point your macro at and step through.
EDIT:
See here for a VBA-only example of doing this:
using a sheet in an excel user defined function