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The Hyperspace is not Always Euclidean: f1 Score in Positive Curvature Fits Best Data - Part IV

F1 score get a boost of up to 79% for the most important class in a support vector machines classification problem. Positive curvature of no

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The Hyperspace is not Always Euclidean: Study of f1 Scores with Sklearn Metrics - Part II

In part I it was explained that a first sight at the f1 scores -considering features as in a classic 3D space against a non-euclidean one- g

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The Hyperspace is not Always Euclidean: Study of f1 Score with Sklearn Metrics - Part III (Colab)

The Hyperspace is not Always Euclidean: Study of f1 Score with Sklearn Metrics, Grid Search - Part III (Google Colab)

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The Hyperspace is not Always Euclidean: Study of f1 Score with Sklearn Metrics - Part I

...this sparse matrix that contains all classes with the most influence, is compared against the 'y_true' that needs also to be...

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MGM Predicted vs Real Quotations (Google Colab): F1 Scores/Confusion Matrix Using Sklearn

Each frequency bin is treated as coordinate feature, then, the hyperplane has as many dimensions as classes are.

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The Accuracy of Splines Overtime - 3D Time-Series Approach Part III: Mastering The Math Matters

In the prior post it was proven that, as a stochastic variable, a stock quote is impossible to be predicted using regression. It is based on

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