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lovely analytics - Lovely Analytics

Tout pour booster sa carrière de data scientist Être Data Scientist ou Data Analyst c’est aussi continuer d’apprendre et d’appliquer de nouvelles méthodes pour exploiter les données. « Je suis Data Scientist et je partage avec vous mes lovely analytics. Ici on parle de méthodes, d’outils et de carrière. » Marie-Jeanne VIEILLE Data scientist les formations en … lovely analytics Lire la suite »

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Topics: decision tree, gradient boosting, grid search, lovely analytics, random forest, random forest sklearn

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Robust & Scalable Application Development, SaaS, Mobile Development, Global Hybrid Delivery Model, ERP Solution - SAP, Oracle & Microsoft Dynamics GP, Tulsa, Oklahoma



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decision-tree.com

decision-tree.com decision-tree.com           
home - Decision Tree
home - Decision Tree
Model of computational complexity. In computational complexity the decision tree model is the model of computation in which an algorithm is considered to be basically a decision tree, i.e., a sequence of queries or tests that are done adaptively, so the outcome of previous tests can influence the tests performed next. Typically, these tests have a small number of outcomes (such as a yes–no question) and can be performed quickly (say, with unit computational cost), so the worst-case time complexity of an algorithm in the decision tree model corresponds to the depth of the corresponding decision tree. This notion of computational complexity of a problem or an algorithm in the decision tree model is called its decision tree complexity or query complexity. Decision trees models are instrumental in establishing lower bounds for complexity theory for certain classes of computational problems and algorithms. Several variants of decision tree models have been introduced, depending on the computational model and type of query algorithms are allowed to perform.

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decisiontree.nl

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t_kahi’s blog
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