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Fundamental Principles of Machine Learning, When we evaluate the outputs…
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When we evaluate the outputs generated from machine learning algorithms and determine their effectiveness.
Normally used as both the inputs and outputs of machine learning algorithm, this is the result of the algorithm operating and interacting with other systems. The data generated from machine learning algorithm will be used to inform the performance and capabilities of said algorithm.
The mathematical model used to replicate or simulate the neural processes that run on the principles of machine learning.
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An algorithm that acts as a control group for a ML model. It can be used to guess the median or mean of a dataset but is generally used to determine the performance characteristics of a given model.
Balanced training sets are important for presenting a more even or at least unbiased dataset that would not influence the algorithm
Neural networks get their namesake by replicating the logic and function of the same neurons that power our brains.
A decision chart that visualizes the possible outcomes of a machine learning algorithm. It then looks at those outcomes to determine the potential accuracy, precision and recall of the algorithm.
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These metrics are normally used to above the behavior of the line of best fit on a graph. Does the utility for ML algorithms increase when the behavior of the curve line is captured?
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A hierarchical algorithm that arranges items based on their popularity from the top to the bottom of the "tree".