The Method of Processing Gradient Tree Boosting

Gradient Tree Boosting
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Gradient Tree Boosting
* Do a quick online research to find a case study and use it to describe how/where the gradient boosting (GB) modeling method is applied.
Scientists can use Gradient Boosting can for Sentiment Analysis, which plays a significant role in text classification. There is a lot of textual information that requires management and analysis, but it has become an ambiguous task with recent developments (Athanasiou & Maragoudakis, 2017). It is due to the ever-growing amount of information that needs piecing together to achieve an outcome depending on the needs of the existing question on the use of social media or e-commerce purchases made each week, month, or annually (Athanasiou & Maragoudakis, 2017). Additionally, Greek texts are best applicable for the Gradient Boosting in Sentiment Analysis because it gives a critical analysis of Greek from the easy notation to the most difficult one implying that there are ways to interpret the meaning of textual information (Athanasiou & Maragoudakis, 2017). Besides, having significant texts is helpful because of the inability to reason carefully or independently. With a distribution of sentiments, the Gradient Boost is best fitted to its analysis as the technique displays an efficient sampling method (Athanasiou & Maragoudakis, 2017). Therefore, dealing with imbalance issues in the different ideas can be corrected while applying Gradient Boosting in Sentiment Analysis.
* Use your own words to describe the main steps of the (basic) GB. You may refer to the slides titled of Lecture 7 with Generalisation to arbitrary differentiable loss function.
A differentiable loss function contains the loss and gradient function as th...