Machine learning in trading: theory, models, practice and algo-trading - page 3552
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That's good to hear.
There seems to be an unconscious internalisation going on :)
I don't know what was so difficult to verbalise?
Why is it pleasant? ))
there is no internalisation, because these words already mean something else in your head :) memory cells are already reserved
Why nice? ))
Because I've been saying for a long time that CV with our data doesn't have the same effect as on representative samples, and now that we've realised that, there will be less controversy further down the line, which is nice.
is not learnt because these words already mean something else in your head :) memory cells are already reserved
Let's take such a term as an example.
Because I've been saying for a long time that CV with our data doesn't have the same effect as on representative samples, and now that they've realised that, there's less controversy further down the line, which is nice.
Let's take a term like this as an example.
Well, maybe in the current application it is not very good... I even know why, it takes a long time to explain.
Let's do it after the weekend.
Because I've been saying for a long time that CV with our data doesn't have the same effect as on representative samples, and now that they've realised that, there's less controversy further down the line, which is nice.
Let's take a term like this as an example.
It's kind of your topic
It's kind of your thing.
Well such a thing, a couple of off-the-shelf features - yes I use something like that.
Thanks for thinking about my needs.
Well that kind of thing, a couple of off-the-shelf functions - yes I use something like that.
Thanks for thinking about my needs.
cabin discretisation
Binarisation is one of the final variants of the whole process.
Quantisation and discretisation are related but not identical concepts that are often used in data processing.
Quantisation
Quantisation is the process of converting continuous values into discrete values, often in the context of digital signal processing. In quantisation, a continuous signal or data is divided into defined levels and each level is assigned a fixed value. This process is used, for example, when converting an analogue signal into a digital signal.
Examples of quantisation:
Discretisation
Discretisation is a broader term that includes quantisation, but can also refer to dividing data into separate categories or bins. Discretisation is used to convert continuous data into categorical or discrete intervals.
Examples of discretisation:
Comparison
Quantisation:
Discretisation:
In the context of machine learning and data analytics, discretisation is often used to prepare data before using it in models, especially if the models perform better with categorical features. Thus, we can say that quantisation is a form of discretisation specialised for processing signals and data with fixed levels.
The term "quantum cutoff" is not a standard or widely used term in science or engineering. However, it can be assumed that it may refer to the context of quantisation in data or signal processing. In such a case, a "quantum cutoff" can be interpreted as an interval or range of values that is assigned to a particular quantum level during the quantisation process.
Quantisation and quantum levels
In the process of quantising a continuous signal or data, the range of possible values is broken down into discrete levels called quantum levels. Each quantum level corresponds to a specific range of values of the original continuous signal.
Quantum cutoff
If we speak of a "quantum cutoff" in this context, it is:
Example
Let's consider an example with quantisation of continuous data in the interval [0, 10] into 5 quantum levels:
Each of these quantum intervals will be assigned a corresponding quantum level, which can be represented, for example, by the numbers 1, 2, 3, 4, and 5.
Thus, a "quantum segment" can be considered as an interval of values, which is converted into a certain discrete level in the process of quantisation.
The above is almost all ChatGPT. I publish it to the fact that since the model understands everything correctly, the term occurs in this context.
I don't mind if you use similar terms to decode my messages, but it doesn't mean that I will change mine - I wrote about it in my articles - you would have read and understood it long ago.