Motivation behind boosting algorithms

Key Takeaways

so in the previous part of the video uh you remember that we have discussed in detail regarding the difference between these three techniques right emble techniques bagging boosting and uh stacking the important thing before I will jump towards the uh algorithm side uh in the very first part we will start with edab Boost I just want to give you a motivation why actually this algorithm studying is required what is the motivation actually behind this boosting algorithms and is that genuinely useful or can we skip this uh portion Al together because already we have covered up so many algorithms in our previous modules so let me give you a few uh answers here and then maybe we can start in the upcoming videos the discussion behind the uh you can Sayo algorithm the very first thing which I hope that you have learned from this introductory module of boosting is that this specific algorithm simply works by combining weak Learners right that's why we are saying that it's a sequential learning algorithm so it's just that if let's say we have multiple models working together again we are not here relying on a single model result and that is the main reason why it is called emble what we are doing is we are trying to uh see that wherever the the model is going wrong we're using other model to train that wrong things so we are somehow rectifying our models and trying to curate a at the end model which is a strong learner right that is the whole approach towards this boosting algorithm that we have discussed still so far right now what is uh the motivation behind that because with this approach it actually improvise the robustness of the model as well as the accuracy of the model that's why you will observe that in almost all the domains in machine learning whether you're talking about banking domain or you're talking about uh you know uh Healthcare domain fintech domain almost everywhere in machine learning these boosting algorithms are being used internally you will see that in the companies there is already a pipeline which is set and people usually definitely try to go for either endom forest or XG boost both of them because they will provide a comparative results all together as comparable to the simple techniques that we have explored in the starting of the course right now the next important part which you will observe in this particular algorithm is the concept of bias variance tradeoff as we have discussed right so what is happening is somehow with the help of this algorithm my model will be able to reduce the biasness my my model will be able to reduce the problem of over sorry underfeeding where if my model is not able to understand the simpler features in the very first go it's okay we will be having multiple variations of uh the models and at every point of time whatever things my first model is not understanding my second model will be able to understand my second model is not understanding my third model will be able to understand so what is happening is we are trying to reduce the bias and variance getting the ideal situation all together so variance is also in a control and bias is Al bias is also we are able to improvise last thing all this process that we have explored till so far in the boosting technique will improves the will definitely improves the generalization of the model and if you remember from the very starting of the course I'm talking about this term a lot that at the end of the day what we want is we want a model which is a good generalized model right which is basically able to uh give us a better result on the Unseen data test data that we have right so that will only be feasible if we will be having a generalized uh model Al together right then only we will be able to get a efficient result then only we will be able to get a good accuracy I would say and that is the core idea that why we are discussing the boosting algorithms in a lot more detail in the upcoming section of the videos so I hope that now you you have a great motivation to start so I will see you all on the upcoming video where now we will directly jump towards the internal intuition behind the edab Boost algorithm