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#### Flashcard 6073230953740

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#SVM
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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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Still effective in cases where number of dimensions is greater than the number of samples.

status measured difficulty not learned 37% [default] 0
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

#### Annotation 6073235410188

 #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.

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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probability estimate
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

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#SVM
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Versatile
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status measured difficulty not learned 37% [default] 0
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

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

status measured difficulty not learned 37% [default] 0
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

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Scalability