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Token Metrics Machine Learning
Token Metrics Machine Learning
Machine learning and quantitative features of Token Metrics explained.
17 articles in this collection
Written by
Aagam Jain,
Jad Hajali,
and
Chase Allred
Crypto Portfolio Strategies: How to Invest in Cryptocurrency With AI
How Token Metrics uses AI to make you money
Written by
Chase Allred
Updated over a week ago
How Token Metrics Uses AI
Guide on How Token Metrics uses Machine Learning
Written by
Jad Hajali
Updated over a week ago
Price Predictions vs Monthly TA Trends
Differences between modeling of price predictions and scoring of monthly TA Trends
Written by
Chase Allred
Updated over a week ago
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.
Written by
Aagam Jain
Updated over a week ago
Root Mean Square Error (RMSE)
The standard deviation of the residuals (prediction errors).
Written by
Aagam Jain
Updated over a week ago
Correlation
A statistic that measures the degree to which two variables move in relation to each other.
Written by
Aagam Jain
Updated over a week ago
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.
Written by
Aagam Jain
Updated over a week ago
Stacking
An ensemble learning technique that combines several machine learning techniques into one predictive model.
Written by
Aagam Jain
Updated over a week ago
Loss Function
A method of evaluating how machine learning techniques work on the training data.
Written by
Aagam Jain
Updated over a week ago
Back Propagation
An algorithm that looks for the minimum value of the error function in weight space.
Written by
Aagam Jain
Updated over a week ago
Supervised Learning
The machine learning task of learning a function that maps an input to an output based on example input-output pairs.
Written by
Aagam Jain
Updated over a week ago
Unsupervised Learning
A type of self-organized learning that helps find previously unknown patterns in data set without pre-existing labels.
Written by
Aagam Jain
Updated over a week ago
Initial Population
The process begins with a set of random individuals which is called a Population.
Written by
Aagam Jain
Updated over a week ago
Fitness Function
The fitness function determines the ability of an individual to compete with other individuals.
Written by
Aagam Jain
Updated over a week ago
Selection
During each successive generation, a portion of the existing population is selected to breed a new generation.
Written by
Aagam Jain
Updated over a week ago
Crossover
Crossover is used to combine the genetic information of two parents to generate a new offspring.
Written by
Aagam Jain
Updated over a week ago
Mutation
The random change in the chromosome, which make the genes of children a little different from its parents.
Written by
Aagam Jain
Updated over a week ago