Machine learning in trading: theory, models, practice and algo-trading - page 1307
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an addict?
Didn't like to deny myself pleasures here and now (cheating, for example). I had a dog, I loved her... she ate a piece of meat I ate her...
not about myself, just an analogy about the here and now
didn't like to deny myself pleasures here and now (cheating, for example). I had a dog, I loved her... she ate a piece of meat I ate her...
Not about me, just the analogy of the here and now.
well, earth to her fluff, she's past(past), and we are here and now:)
It doesn't fit.
Although they called it a test kit, but in the description they write that it is used for validation. Here is Maxim's quote:
-t
--test-set
A comma-separated list of input files that contain the validation dataset description (the format must be the same as used in the training dataset).
Omitted. If this parameter is omitted, the validation dataset isn't used.
So validation is a check, but I divide validation into two types - model checking during training and model checking independent of training.
Validation and test are essentially synonymous.So validation is a check, but I divide validation into two types - model checking during training and model checking independent of training.
Validation and test are essentially synonymous.But it is the term validation that is used in packages. Besides, this is English and there may be peculiarities of terminology there.
To me, a test is just looking through. While validation takes an active part in the learning process and its stopping. Not synonyms at all if you look at the essence implicit in these terms.
But it is the term validation that is used in the packages. Besides, this is English, there may be subtleties of terminology.
it is latin at all ))
But it is the term validation that is used in the packages. Besides, this is English, and there may be subtleties of terminology.
To me, a test is just a look. And validation takes an active part in the process of learning and stopping it. Not at all synonymous if you look at the essence embedded in these terms.
I do not know the English language that deeply. However, in Russian both test and validation are often synonymous, so I like the term "exam sampling". Also, for some MO methods the test/validation is needed directly for learning, while for others it's just a simple look. Anyway, I'm not imposing anything.
I don't know the English language that deeply. However, in Russian both test and validation are often synonymous, so I like the term "exam sampling. Also, for some MO methods the test/validation is needed directly for learning, while for others it is just a simple look. In general, I am not imposing anything.
Why don't you explain your personal definitions at the end of each post?
How can you not impose, if you say that the validation (for everyone) section you will call the test section. And we should keep that in mind when reading your posts. And apply your personal terminology instead of the common international terminology.
Then at the end of each post let's decipher the terms you personally apply.
Where is this ISO standard for application?
Yes, and every time I try to write about which sample we are talking about, precisely because I know the peculiarities of catbust.
Where is this ISO standard for application?
Yes, and I try to write every time which sample we are talking about, precisely because I know the peculiarities of catbust.
ISO is not released for everything). In this case, you could be guided by what the MO pacts call the 2nd section.
In the catbust you use, even though they wrote test, they write in the explanation that it is used for validation. In other packages XGBoost, Darch - they write validation.
So validation is validation, but I divide validation into two types - model validation in training and validation independent of training.
Validation and test are essentially synonymous.Validation set is involved in training. It is used to adjust model parameters during training. In some packages the validation set is not required, in this case the training set is divided into train/valid in some proportion in the fit() function. But it is better to set it yourself.
The test set is used to check the quality of the trained model and this data should not be seen by the model during training.
So these are still different things, no need to confuse them.
Good luck