Token Metrics Machine Learning

Machine learning and quantitative features of Token Metrics explained.

Jad Hajali avatar Chase Allred avatar Alejandro Bonilla avatar
17 articles in this collection
Written by Jad Hajali, Chase Allred, and Alejandro Bonilla
Data Science and Machine Learning Terms

Mean Absolute Error (MAE)

An average of the absolute errors |ei| = |yi| - |xi|, where yi is the prediction and xi the true value.
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Root Mean Square Error (RMSE)

The standard deviation of the residuals (prediction errors).
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Correlation

A statistic that measures the degree to which two variables move in relation to each other.
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Multi Layer Perceptron Model (MLP)

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

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

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

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

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

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

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

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

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

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

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