**Remember that when an interactice chart is cited on the post, by clicking on it the source code will be shown, In order to visualize it on the right way, download the file as html and open it with your browser.**

**Remember that when an interactice chart is cited on the post, by clicking on it the source code will be shown, In order to visualize it on the right way, download the file as html and open it with your browser.**

In **part I** it was explained that a first sight at the

**-considering features as in a**

*f1 scores***against a**

*classic 3D space***one- gave clues of the**

*non-euclidean***-price changes- to be moving in a non-euclidean hyperspace rather than the classic cartesian one. It was based on the f1 scores being far**

*variable***for the former than for the latter one i.e a far**

*higher***under such conditions.**

*better fit*Therefore, it would explain why ** most** of models trying to

**price changes in the stock exchange**

*predict***badly as they use**

*fail***cartesian features. Consequently, as not all readers are familiarized with what a non-euclidean hyperspace means, a brief explanation besides the basis of this theory will be explained. First, let's imagine we have no idea of this concept and we define**

*classic***in a classic space as shown below:**

*n features*Under this assumption, when running the model explained in part I and using **Sklearn Grid Search** for finding the best parameters, the

**and f1 scores obtained with the**

__confussion matrix__**are as follows:**

__classification report__F1 scores from 0.16 up to 0.73 -class 0- with a half accuracy and wighted f1 score. Results are ** not optimistic** so let's see how they change when assuming a non-euclidean reality. In such space, two

**lines will either**

*parallel***or**

*converge***whether the surface has a**

*diverge***or**

*positive***curvature respectively, better denoted as**

*negative***and**

*+k***in most literature. Actually, in other areas as physics, it is only used to describe the**

*-k***:**

__shape of the universe__If a ** positive curvature** is assumed, it means that features are

**to a new spherical like reality as shown below:**

*translated*The f1 scores and classification under such conditions are:

On the other hand, if a negative curvature is assumed, the translation would be as follows:

And the f1 scores and classification under such conditions are:

As seen, the ** best fit** is for the features under a

**surface with f1 scores from**

*+k***to**

*0.4***with weighted accuracy and f1 scores of**

*0.9***and**

*0.64***respectively.**

*0.62*For details on the python program used access the post **here**.

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