Algorithm Families of Machine Learning Algorithms

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The broad algorithmic family classification that groups similar machine learning approaches based on their core mathematical foundations and learning mechanisms

Meta-Learning: Meta-learning algorithms learn how to learn by extracting knowledge from multiple learning tasks.
Quantum Models: Quantum model algorithms utilize quantum mechanical properties to enhance learning and optimization processes.
Ensemble Methods: Ensemble method algorithms combine multiple models to achieve better predictive performance than individual models.
-: Machine learning algorithms without specific family classification, ranked by their performance scores.
Instance-Based: Instance-based algorithms make predictions by comparing new data points to stored training examples.
Probabilistic Models: Probabilistic model algorithms use statistical distributions to represent uncertainty and make probabilistic predictions.
Tree-Based: Tree-based algorithms use decision tree structures to make predictions through hierarchical decision rules.

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