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Sep 11, 20170183;32;In this article, we looked at one of the supervised machine learning algorithm Naive Bayes mainly used for classification. Congrats, if youve thoroughly amp; understood this article, youve already taken you first step to master this algorithm. From here, all you need is practice.

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As Steffen said, the confusion matrix could be a good way to estimate the quality of a classifier. In the binary case, you can derive some measure from this matrix such as sensitivity and specificity, estimating the capability of a classifier to detect a particular class.

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Support vector machine (SVM) is a linear binary classifier. The goal of the SVM is to find a hyper plane that separates the training data correctly in two half spaces while maximising the margin between those two classes.

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Sep 11, 20170183;32;In this article, we looked at one of the supervised machine learning algorithm Naive Bayes mainly used for classification. Congrats, if youve thoroughly amp; understood this article, youve already taken you first step to master this algorithm. From here, all you need is practice.

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Specifies the kernel type to be used in the algorithm. It must be one of linear, poly, rbf, sigmoid, precomputed or a callable. If none is given, rbf will be used. If a callable is given it is used to pre compute the kernel matrix from data matrices; that matrix

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That is the classifier itself is inherently parallel. It is just a collection of distinct decision trees after all. In relation to classifiers that use scipy which itself might be parallelized, that is very interesting although ideally the user would always have some control over how many cores are used on the machine.

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For evaluate a scoring classifier at multiple cutoffs, these quantities can be used to determine the area under the ROC curve (AUC) or the area under the precision recall curve (AUCPR). All of these performance measures are easily obtainable for binary classification problems. Which measure is appropriate depends on the type of classifier.

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You can display the coding design matrix of a trained ECOC classifier by entering Mdl.CodingMatrix into the Command Window. You should form a coding matrix using intimate knowledge of the application, and taking into account computational constraints.

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Each ensemble algorithm is demonstrated using 10 fold cross validation, a standard technique used to estimate the performance of any machine learning algorithm on unseen data. Bagging Algorithms Bootstrap Aggregation or bagging involves taking multiple samples from your training dataset (with replacement) and training a model for each sample.

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