3B1B notes

Few points that I need to note:

3B1B’s ML explainer videos are pretty good

 They explain the fact that there are in fact three stages of optimization that occurs. First the training data gives us a value for the different stages. Within these stages, weights determine the importance of each variable (I am using the wrong terms – need to look it up)  and the error function gives us variance.

 Mean squared error tells us how far off the prediction is from the actual value – a correct prediction leads to minimization of this value

 Stochastic gradient descent is different from gradient descent wherein it only works on a small random batch of data to figure out the minima and not the entire data or the global minima since minimizing on the entire data is computationally expensive and finding out the global minima is not really possible.

Random Forest Classifier Code

Source: https://www.kaggle.com/code/prashant111/random-forest-classifier-tutorial

-Based on ensemble learning

-Highlights the importance of feature selection - run once, see what is important, remove others, re-run, see test the increase in accuracy

-Remember that random forest can be used for both classifier and regression problems.

-In random forest classifier, the higher the number of trees in the forest, the higher the accuracy