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#SVM

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probability estimate

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probability estimate

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#SVM

Question

probability estimate

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?

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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

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

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 |

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