Hello, Hope you guys are doing well.
I needed information on the time complexity of the models under supervised learning <http://scikit-learn.org/stable/supervised_learning.html#supervised-learning> title. I am looking for this information because- we (my team) are building a platform that allows a user to run multiple models. The models we select to build are based on a couple of user requirements such as time, accuracy and model interpretability. This information will help us in understanding what models not to select for large datasets and so on. More specifically, I am looking for information on these algorithms OLS Elastic Net LARS Bayesian regression Linear Discriminant Analysis SVM (all kernels) Nearest Neighbors regression Decision Trees Random Forest AdaBoost Gradient Tree Boosting Let me know if there's additional information or details you need me to provide -- Regards, Shubham Ashok Gandhi Ph: (+91) 8987419771 <089874%2019771>
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