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multi-cloud data catalogs the easy way using metadata and

multi-cloud data catalogs the easy way using metadata and machine learning by waterline data. data governance is drudgery but in the post-gdpr world it's beyond foundational.

8 tips to stop annoying robocalls - cbs news

about once a month i get a "robocall" from an eye doctor i visited almost three years ago reminding me to schedule an appointment. it amuses me as much as it annoys me. fortunately it's the

classifier comparison — scikit-learn 0.21.3 documentation

classifier comparison¶ a comparison of a several classifiers in scikit-learn on synthetic datasets. the point of this example is to illustrate the nature of decision boundaries of different classifiers. this should be taken with a grain of salt as the intuition conveyed by these examples does not necessarily carry over to real datasets.

machine learning classifier evaluation using roc and cap

machine learning classifier evaluation using roc and cap curves. draw a vertical line at 50% from the x-axis till it crosses the support vector classifier plot. at the point where the vertical line cuts the trained model draw a horizontal line such that it cuts the y-axis.

is there a best machine learning classifier - quora

no free lunch theorem (nfl theorem) [wol96] [wm + 95]: for any learning algorithms la and lb if la is better than lb for some problems then there must be some problems lb is better than la . in other words la and lb have the same performance i

choosing a machine learning classifier

choosing a machine learning classifier. how do you know what machine learning algorithm to choose for your classification problem of course if you really care about accuracy your best bet is to test out a couple different ones (making sure to try different parameters within each algorithm as well) and select the best one by cross-validation

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margin classifier - wikipedia

support vector machine definition of margin. see support vector machines and maximum-margin hyperplane for details.. margin for boosting algorithms. the margin for an iterative boosting algorithm given a set of examples with two classes can be defined as follows. the classifier is given an example pair () where ∈ is a domain space and ∈ = {− +} is the label of the example.