Ensemble methods are like the anti-hero of machine learning: they're not the most elegant solution, but they'll get the job done.
y = f(x) + g(x) + h(x) + ... + z(x)
Where y is the final answer, f(x) is the first model, g(x) is the second model, and h(x) is the third model. And so on.
It's like a never-ending party with multiple models, each trying to outdo the other, until they all agree on a single, slightly-better-than-average result.
But wait, there's more!
// Example of a simple ensemble model in JavaScript
var models = [
new Model1(),
new Model2(),
new Model3()
];
var result = 0;
for (var i = 0; i < models.length; i++) {
result += models[i].predict(input);
}