Question - How to choose an algorithm for a problem?
Answer -
To solve a problem, there can be multiple Machine Learning algorithms with different approaches and constraints. However, a generic approach can be applied to most of the problems and find a suitable algorithm. Below are the steps you need to consider while choosing an algorithm:
Categorize the Problem
The first is finding the algorithm, which is to categorize it based on the type of input you have and the output you want from it. If the data is labeled, it’s a problem for supervised learning. If the data is not labeled, then it’s an unsupervised learning problem. At last, if the problem aims to optimize a model, then it’s a reinforcement learning problem.
Similarly, you can categorize a problem based on the outcome you want from the algorithm. If the output is expected to be numerical then it’s a regression problem. Is class is the output of a model, it’s a classification problem, and grouping of the input values can be categorized as the clustering problems.
Understanding the Data
Data plays an important role in the process of selecting the right algorithm for your problem. This is because, some algorithms can process tons of data, while some work better with smaller samples. Analyzing and transforming your data will also help you to know the constraints and the challenges you t=want to overcome while solving the problem.
Find the available Algorithms
Identify the available algorithms you can apply for solving the problem in a reasonable timeframe. Some of the factors that may affect your choice of the right algorithm include the accuracy of the algorithm, complexity, scalability interpretability, build & training time, space, and the time it takes to solve the problem.
Implement the Algorithm
After selecting the algorithm, you have to make an evaluation criteria by carefully selecting the testing values and subgroups of the datasets. Also, check the time taken by each algorithm to solve the problem. The algorithm that provides accurate results in the given time while acquiring less space, would be the best algorithm for your problem.