General structure for models

[mt0rm0]

For the Models section, I think it could be interesting using a common structure so the information can be found easily. My notes about algorithms are based on Jason Brownlee ideas (https://machinelearningmastery.com/how-to-learn-a-machine-learning-algorithm/) and structured as follow:

Algorithm name

• 1. Taxonomy (How can you classify this algorithm? What is the objective or goal for the algorithm?)
• 2. Strategy (What is the information processing strategy of the algorithm? What metaphors or analogies are commonly used to describe the behavior of the algorithm?)
• 3. Procedure (Pseudocode for explaining )
• 4. Heuristics (What are the heuristics or rules of thumb for using the algorithm? What classes of problem is the algorithm well suited?)
• 5. Usual hyperparameters (Which hyperparameters are usually used and in which range?)
• 6. Code listing (Usage example. What are common benchmark or example datasets used to demonstrate the algorithm?)
• 7. References and bibliography (What are useful resources for learning more about the algorithm? What are the primary references or resources in which the algorithm was first described?)