If you have ever trained a decision tree and noticed that it performs very well on the training data but poorly on unseen data, you have already encountered the core weakness of trees: overfitting.
Wow, the way you highlighted how Random Forests combine the interpretability of decison trees with the power of ensemble learning really resonated with me.
ODB versus personal databasing. I like when entrepreneurship becomes group authenticity and recordable evidence of trial, trial and success! Scientific practices learning to expunge human errors!🎊🦖
Wow, the way you highlighted how Random Forests combine the interpretability of decison trees with the power of ensemble learning really resonated with me.
ODB versus personal databasing. I like when entrepreneurship becomes group authenticity and recordable evidence of trial, trial and success! Scientific practices learning to expunge human errors!🎊🦖
Highly informative!!
I have a few silly doubts.
If we define bootstrap as false, then how will the model evaluate because we don't have a OOB sample in this case?