Come gather round young ones,
Let me tell you a tale,
Of classifiers and boundaries,
And algorithms that prevail.

There's a method called bagging,
Where samples are combined,
And a classifier is trained,
To make accurate design.

But sometimes there's a bias,
And accuracy can sway,
So another method called boosting,
Helps correct it in its way.

Bayes is a fancy word,
For a probability guess,
It helps find the centroid,
And keeps the model progress.

We talk of classes and boundaries,
To keep things all in line,
And a batch of data to train on,
Makes accuracy just fine.

So there you have it little ones,
A glimpse of what we do,
To make our models perfect,
And our predictions true.

Reflections

1. What is machine learning and why is it important?
2. Can you name some of the techniques and concepts introduced in the poem?
3. How might machine learning be used in real-world situations?
4. Can you think of a problem that you would like a computer to help solve?
5. Do you think machine learning can be biased? Why or why not?
6. What other areas of science and technology do you find interesting?