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Flashcard 6073230953740

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#SVM

Question

advantages

Answer

[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 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

Tags

#SVM

Question

[default - edit me]

Answer

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. 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.

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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: 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

Tags

#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 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. 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.

status	not learned	measured difficulty	37% [default]	last interval [days]
repetition number in this series	0	memorised on		scheduled repetition
scheduled repetition interval		last repetition or drill

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: 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 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

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on 15-Dec-2020 (Tue)

Flashcard 6073230953740

Flashcard 6073232002316

Annotation 6073235410188

Flashcard 6073236983052

Flashcard 6073238293772

Flashcard 6073239342348

Flashcard 6073245633804