Token Metrics

Guide on How Token Metrics uses Machine Learning

How Token Metrics Uses AI

Differences between modeling of price predictions and scoring of monthly TA Trends

Machine learning and quantitative features of Token Metrics explained.

An average of the absolute errors |ei| = |yi| - |xi|, where yi is the prediction and xi the true value.

The standard deviation of the residuals (prediction errors).

A statistic that measures the degree to which two variables move in relation to each other.

An MLP consists of at least three layers of nodes: an input layer, a hidden layer and an output layer.

An ensemble learning technique that combines several machine learning techniques into one predictive model.

A method of evaluating how machine learning techniques work on the training data.

An algorithm that looks for the minimum value of the error function in weight space.

The machine learning task of learning a function that maps an input to an output based on example input-output pairs.

A type of self-organized learning that helps find previously unknown patterns in data set without pre-existing labels.

The process begins with a set of random individuals which is called a Population.

The fitness function determines the ability of an individual to compete with other individuals.

During each successive generation, a portion of the existing population is selected to breed a new generation.

Crossover is used to combine the genetic information of two parents to generate a new offspring.

The random change in the chromosome, which make the genes of children a little different from its parents.