I bring thee good news. There are ways of mitigating overfitting!
<ul>
<li>Collect more training data. (but, but, but sometimes, collecting more data can be challenge, especially when they are not available)
</li>
<li>Next thing we might want to consider is to reduce the number of features. You wanna be careful with this since reducing the features too much can result in overfitting -- except, well except if that's what you want. Also the problem with reducing the number of features is that we might not know which exactly of the feature will be crucial.
</li>
<li>Lastly: Regularization.
</li>
</ul>

I bring thee good news. There are ways of mitigating overfitting!

* Collect more training data. (but, but, but sometimes, collecting more data can be challenge, especially when they are not available)
    
* Next thing we might want to consider is to reduce the number of features. You wanna be careful with this since reducing the features too much can result in overfitting -- except, well except if that's what you want. Also the problem with reducing the number of features is that we might not know which exactly of the feature will be crucial.
    
* Lastly: Regularization.

Overfitting? Fear Not, There’s a Way Out!