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guess coffee. However if an entry had 25mg caffeine and cost 150 ₹ , our machine might have
difficulty in guessing tea or coffee and could possibly make a wrong choice. Now, since the
correct answer deviates from the line, the computer refines the model slightly by adjusting the
line and bringing it closer to the correct answer.
The computer thus ‘learns’ from its mistake and improves itself to not repeat it again. The
computer repeats the process over and over with different data, tweaking the model a bit each
time until it is confident of guessing the correct answer with a high degree of probability.
Now, one might say - why not train the model more and more to get the ultimate
guessing machine? Here’s where overfitting comes into play.
Overfitting is where the computer has
been fed too much data pertaining to a
certain case that it starts predicting
answers incorrectly. For example, if you
start including too many tea varieties in
the dataset that have very high caffeine
content, it might incorrectly start
assuming even some coffee varieties to
be tea. Here, the deviations(called noise)
start affecting the computer’s ability to
‘learn’ from the correct answers(called
signals). Underfitting is the opposite,
where our model is too general and
flexible in it’s parameters.
After testing, if necessary, some parameter tuning is done. Finally, we can use our ML
model for what it was intended - real-world predictions. While this may not sound all that difficult,
actual ML models can be incredibly intricate structurally and functionally. Today, the
applications of ML are exponentially snowballing. While we cannot ascertain when AI will
overtake human intelligence, it is unequivocally clear that Machine Learning will pave the way.