If you want to test whether your algorithm works as expected, I'd use sklearn datasets. They allow you to create simple synthetic 2D data with certain properties: circles, half moons, etc.
If you want "real" datasets, here is an interesting resource found after a brief search:
It seems to ...
Can I use any machine learning methods having only one feature?
In fact, many NLP classifications tasks are in this format. Given 1 piece of text, classify something. For example:
Given 1 review, classify the sentiment
Given 1 news article, classify the topic
Given 1 chat message, classify the intent
And now you have:
Given 1 name, classify the ...
"Apriori" algorithm is used for "Association Rules" learning.
In very simple terms its trying to determine that if people who buy chocolates ,do they buy roses also with that? or do they buy chocolate with ice-cream more? or its chocolate+roses+ice-cream always together? or any combination of it.
So, the data which contains these purchase transactions are ...
Do you think this result is right?
Depends what you mean by "right"... the results seem reasonable, I don't see any obvious sign of mistake in the process.
Can you explain what would you understand by looking at this result please?
I observe that you don't have any data for classes 1 and 5, so technically it's a 3-classes problem.
First with 3 ...
Obtaining a dataset is an important part of defining a ML problem but it's not the only one. Typically this involves the following steps:
Define the goal of the problem. Example: predict AZT level of tolerance among AIDS patients.
Obtain appropriate data for the problem.
Design the formal setting of the experiment:
what kind of problem is it (e.g. ...
You can use the ds.map() function to create dataset conatains only images or labels:
ds_images = ds.map(lambda d:d['images')
the original purpose of the map function is manipulating the data without converting to numpy, for example:
ds_images = ds.map(lambda d:d['images']/255)
hope I helped you.
Kaggle has some nice datasets available, including the classic Iris dataset. Take a look and pick one that looks interesting.
There are some impactful real-world data sets there, including COVID-19 related data sets. Something on the lighter side might be this scrubbed Iris data set posted not long ago.
EDIT: to elaborate on COVID-19, Kaggle has the COVID-...
EDIT: It seems I misunderstood the task at first, so here's my correction. Hope it works this time
It seems like what you're trying to do is similar to what is in the documentation under examples/split_data_for_unbiased_estimation.py (or this github issue which seems to be exactly what you want)
The code manually splits the dataset into two without using ...
A useful tool in this regard is Weights & Biases which covers the functionality that you describe. It is not open-source though and only free to use for personal use. It is built to do experiment tracking, and integrates well with other common tools.
The founder was a recent guest on the TWIML AI podcast. In the podcast, he explains the philosophy of ...
There is Deepkit. It's a open-source devtool for machine learning. It tracks experiments, versions code, has a model debugger, infrastructure and project management. It satisfies most of your wishes, is open-source, and crossplatform.
You can use it completely offline, self-host a server, or use the cloud server to store your experiment data.