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Flashcard 6073230953740

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#SVM
Question
advantages
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[default - edit me]

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1.4. Support Vector Machines — scikit-learn 0.23.2 documentation
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







Flashcard 6073232002316

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#SVM
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[default - edit me]
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Still effective in cases where number of dimensions is greater than the number of samples.

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1.4. Support Vector Machines — scikit-learn 0.23.2 documentation
(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







#SVM
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.
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1.4. Support Vector Machines — scikit-learn 0.23.2 documentation
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




Flashcard 6073236983052

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#SVM
Question
probability estimate
Answer
probability estimate

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1.4. Support Vector Machines — scikit-learn 0.23.2 documentation
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







Flashcard 6073238293772

Tags
#SVM
Question
Versatile
Answer
[default - edit me]

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1.4. Support Vector Machines — scikit-learn 0.23.2 documentation
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







Flashcard 6073239342348

Tags
#SVM
Question
[default - edit me]
Answer
different Kernel functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels.

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1.4. Support Vector Machines — scikit-learn 0.23.2 documentation
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







Flashcard 6073245633804

Question
Scalability
Answer
[default - edit me]

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2.3. Clustering — scikit-learn 0.23.2 documentation
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