In machine learning, there’s something called the “No Free Lunch” theorem. In a nutshell, it states that no one algorithm works best for every problem, and it’s especially relevant for supervised learning (i.e. predictive modeling).
For example, you can’t say that neural networks are always better than decision trees or vice-versa. There are many factors at play, such as the size and structure of your dataset.
As a result, you should try many different algorithms for your problem, while using a hold-out “test set” of data to evaluate performance and select the winner.
Of course, the algorithms you try must be appropriate for your problem, which is where picking the right machine learning task comes in. As an analogy, if you need to clean your house, you might use a vacuum, a broom, or a mop, but you wouldn’t bust out a shovel and start digging.
The Big Principle
However, there is a common principle that underlies all supervised machine learning algorithms for predictive modeling.
Machine learning algorithms are described as learning a target function (f) that best maps input variables (X) to an output variable (Y): Y = f(X)