Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Classification and regression tree learning on massive datasets is a common data mining task at Google, yet many state of the art tree learning algorithms require training data to reside in memory on a single machine. While more scalable implementations of tree learning have been proposed, they typically require specialized parallel computing architectures. In contrast, the majority of Google's computing infrastructure is based on commodity hardware.#R##N##R##N#,  describe PLANET: a scalable distributed framework for learning tree models over large datasets. PLANET defines tree learning as a series of distributed computations, and implements each one using the MapReduce model of distributed computation.  show how this framework supports scalable construction of classification and regression trees, as ll as ensembles of such models.  discuss the benefits and challenges of using a MapReduce compute cluster for tree learning, and demonstrate the scalability of this approach by applying it to a real world learning task from the domain of computational advertising.
['Biswanath Panda', 'Joshua S. Herbach', 'Sugato Basu', 'Roberto J. Bayardo']
I am writing a research paper on a idea PLANET: massively parallel learning of tree ensembles with MapReduce
[{"from": "human", "value": "I am writing a research paper on a idea PLANET: massively parallel learning of tree ensembles with MapReduce"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Classification and regression tree learning on massive datasets is a common data mining task at Google, yet many state of the art tree learning algorithms require training data to reside in memory on a single machine. While more scalable implementations of tree learning have been proposed, they typically require specialized parallel computing architectures. In contrast, the majority of Google's computing infrastructure is based on commodity hardware.#R##N##R##N#,  describe PLANET: a scalable distributed framework for learning tree models over large datasets. PLANET defines tree learning as a series of distributed computations, and implements each one using the MapReduce model of distributed computation.  show how this framework supports scalable construction of classification and regression trees, as ll as ensembles of such models.  discuss the benefits and challenges of using a MapReduce compute cluster for tree learning, and demonstrate the scalability of this approach by applying it to a real world learning task from the domain of computational advertising."}]