Extreme Gradient Boosting

The term ‘Boosting’ refers to a family of algorithms which converts weak learner’s to strong learners.

How would you classify an email as SPAM or not? Like everyone else, our initial approach would be to identify ‘spam’ and ‘not spam’ emails using following criteria. If:

  1. Email has only one image file (promotional image), It’s a SPAM
  2. Email body consist of sentence like “You won a prize money of $ xxxxxx”, It’s a SPAM
  3. Email from known source, Not a SPAM

Above, we’ve defined multiple rules to classify an email into ‘spam’ or ‘not spam’. But, do you think these rules individually are strong enough to successfully classify an email? No.

Individually, these rules are not powerful enough to classify an email into ‘spam’ or ‘not spam’. Therefore, these rules are called as weak learners.To convert a weak learner to a strong learner, we’ll combine the prediction of each weak learner to form one definitive strong learner.

In gradient boosting, it trains many model sequentially. Each new model gradually minimises the loss function (y = ax + b + e, e needs special attention as it is an error term) of the whole system using Gradient Descent method. The learning procedure consecutively fit new models to provide a more accurate estimate of the response variable.

The principle idea behind this algorithm is to construct new base learners which can be maximally correlated with negative gradient of the loss function, associated with the whole ensemble.

So how did we code it in the example of UCI adult data set , well check out the link below->

https://github.com/mmd52/UCI_ADULT_DATSET_MODELS_RUN/blob/master/XGBOOST.R

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