How to choose the right algorithm for your ML model?

We have different algorithms to solve the tasks. The common algorithms used to perform classification, regression, clustering, and density estimation tasks are:

You might ask yourself, "If these do the same thing, why are so many methods? Why can't I choose any one method?" I will tell you the reasons.

First, consider your goal. I mean, what are you trying to get out of your data? Like do you want to find the groups of people who buy popcorn along with the sandwich, or do you want a probability that it might rain today at your place? Considering your goal is your big task. If you are trying to predict something or forecast a value, you need to choose supervised algorithms. If not, obviously, it will be unsupervised algorithms.

Second, go for your target value. If it is discrete (Yes/No, Red/Black/Grey, 1/2), you need to choose 'Classification'. If not, or if the value takes on a number like (99, 12), then you need to go for 'Regression'. If you’re not trying to predict a target value, then you need to look into unsupervised learning. Are you trying to fit your data into some discrete groups? If so, and that’s all you need, you should look into 'Clustering'. Do you need to have some numerical estimate of how strong the fit is in each group? Then you probably should look into a density estimation algorithm.

Third, you should spend some time getting to know about your data, and the more you know about it, the better you will train the model. When you analyze your data, you should clarify these

  • Are the features nominal or continuous?

  • Are there any missing values in the features?

  • If there are missing values, why are there?

  • Are there any outliers in the data?

  • Are there any duplicate values?

  • Are the data in a standardized format? If not, check for data transformation [Normalizing to common scale, encoding the categorical if necessary, transforming skew data]

  • Do we need all the features in the data? If not, what are the features are to be selected [Removing irrelevant or redundant features]

These questions about your data can help you narrow the algorithm selection process. From my point of view, there is no single answer to what the best algorithm is or what will give you the best results. Try out using different algorithms and check their performance. You can improve the performance of the algorithm using various techniques.

So, finding the best algorithm for your model is an iterative process of trial and error.