To identify a Machine Learning Algorithm for a particular problem, the following steps should be followed:
Step 1: Problem classification: Classification of the problem depends on the classification of input and output:
Classifying the input: Classification of the input depends on whether there is data labeled (supervised learning) or unlabeled (unsupervised learning), or whether a model has to be created that interacts with the environment and improves itself (reinforcement learning.)
Classifying the output: If the output of a model is required as a class, then some classification techniques need to be used.
If the output is a number, then regression techniques must be used; if the output is a different cluster of inputs, then clustering techniques should be used.
Step 2: Checking the algorithms in hand: After classifying the problem, the available algorithms that can be deployed for solving the classified problem should be considered.
Step 3: Implementing the algorithms: If there are multiple algorithms available, then all of them are to be implemented. Finally, the algorithm that gives the best performance is selected.