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4.8. Implementation details 1.4. Support Vector Machines¶ Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection. The <span>advantages of support vector machines are: Effective in high dimensional spaces. Still effective in cases where number of dimensions is greater than the number of samples. Uses a subset of trainin

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(SVMs) are a set of supervised learning methods used for classification, regression and outliers detection. The advantages of support vector machines are: Effective in high dimensional spaces. <span>Still effective in cases where number of dimensions is greater than the number of samples. Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient. Versatile: different Kernel functions can be specified for the decisi

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ernel functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels. The disadvantages of support vector machines include: <span>If the number of features is much greater than the number of samples, avoid over-fitting in choosing Kernel functions and regularization term is crucial. SVMs do not directly provide probability estimates, these are calculated using an expensive five-fold cross-validation (see Scores and probabilities, below). The support vector machines

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s include: If the number of features is much greater than the number of samples, avoid over-fitting in choosing Kernel functions and regularization term is crucial. SVMs do not directly provide <span>probability estimates, these are calculated using an expensive five-fold cross-validation (see Scores and probabilities, below). The support vector machines in scikit-learn support both dense (numpy.ndarray

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ve in cases where number of dimensions is greater than the number of samples. Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient. <span>Versatile: different Kernel functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels. The disadvantages of support vect

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where number of dimensions is greater than the number of samples. Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient. Versatile: <span>different Kernel functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels. The disadvantages of support vector machines include: If the number of features is much greater than the number of samples, avoid over-fitting in choosing Kernel functions and regulariz

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n be obtained from the functions in the sklearn.metrics.pairwise module. 2.3.1. Overview of clustering methods¶ A comparison of the clustering algorithms in scikit-learn¶ Method name Parameters <span>Scalability Usecase Geometry (metric used) K-Means number of clusters Very large n_samples, medium n_clusters with MiniBatch code General-purpose, even cluster size, flat geometry, not too many clu