In this video, I'll be talking about how to avoid overfitting the models in machine learning. Strategy number 5. Feature selection. It's as simple as that. You're preparing for an exam of SAT. You go to the library, you will only get the books related to SAT. Not all the other books. That is what feature selection means. Only select the features which bring in value to your model. Strategy number 6 are least stopping. You don't want your model to learn all the noise and the random fluctuations in the data. You want it to stop learning as soon as it has learned all the key bits of it. Strategy number 7. Regularization. For linear models, you can use Lasso or Ridge. And for non-linear models, in neural networks, you can add draw-out layers. Strategy number 8. Insampling. Every model has its pros and cons. Pros of one model overcomes the cons of the other one. Why just train one model? Train several different models and make an aggregate out of it. That is the reason why several different bagging and boosting methods excel in a lot of machine