How to properly predict date using Orange 3 First of all - I'm new to all of this.

I'm trying to create a model to predict the release of ios 12 based on previous years. I've got an excel that has a format like this:
ios version | release name | date

Each version consists of about 5 betas + general release.
I've set up the import like this:


Secondly I've set up flow like this:


First step selects all the rows that have [date] param - those go to either kNN or Linear Regression, everything else (1 row with ios 12 release date) goes to prediction and then to a table.

Cool, now depending on the modeling I get different results (as expected). It's either 2015-09-17 (kNN) or 2018-09-10 (LR). I've used the Test &amp; Score, which gives me:



If I'm reading this properly - Linear Regression is more accurate (R2 column), meaning that iOS 12 will release on 10th of Sept 2018  (DUH! not 2015).

But if I look at release dates up to now, this might be wrong, because no other release happened so soon:



So I also did an exercise where I've adjusted the dates, so that they are all in 2018 (so that the year is less important, and I'd care more about relation within each year, given the features) - here are predicted results:



and Test &amp; Score for the offsets:


Now... I would welcome all comments.
Am I using the wrong modeling? Something other than kNN &amp; LR? Should I be using offsets? Am I using the tool completely incorrectly?
So you have two independent variables. iOS version and release name, which is actually a type. Both are categorical and only one has any predictive power, because iOS version you are trying to predict does not appear in your training set. Thus, effectively, you are predicting based on the release type only and only the "release" category is actually meaningful because this is what you are trying to predict.

I can hardly think of a model that would perform ok with such limited information. Anyway, regression models will always give you some answer, even if your input is unsound.

I guess the better features would be:


month
day of month
days since last release
months since last release
years since last release
days since beta
months since beta
years since beta


This type of thinking. Then just by looking at the data you would clearly see that prod releases of iOS happen always in mid Sept each year. No need for model to figure it out :) Any method would suffice, especially kNN or least squares.