Flow

sklearn.svm.classes.SVC

Visibility: public Uploaded 11-11-2019 by Jan van Rijn
sklearn==0.20.0
numpy>=1.6.1
scipy>=0.9 3 runs

0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads

0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads

Issue | #Downvotes for this reason | By |
---|

C | Penalty parameter C of the error term | default: 0.5 |

cache_size | Specify the size of the kernel cache (in MB) class_weight : {dict, 'balanced'}, optional Set the parameter C of class i to class_weight[i]*C for SVC. If not given, all classes are supposed to have weight one The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` | default: 200 |

class_weight | default: null | |

coef0 | Independent term in kernel function It is only significant in 'poly' and 'sigmoid' | default: 0.0 |

decision_function_shape | Whether to return a one-vs-rest ('ovr') decision function of shape (n_samples, n_classes) as all other classifiers, or the original one-vs-one ('ovo') decision function of libsvm which has shape (n_samples, n_classes * (n_classes - 1) / 2). However, one-vs-one ('ovo') is always used as multi-class strategy .. versionchanged:: 0.19 decision_function_shape is 'ovr' by default .. versionadded:: 0.17 *decision_function_shape='ovr'* is recommended .. versionchanged:: 0.17 Deprecated *decision_function_shape='ovo' and None* | default: "ovr" |

degree | Degree of the polynomial kernel function ('poly') Ignored by all other kernels | default: 3 |

gamma | Kernel coefficient for 'rbf', 'poly' and 'sigmoid' Current default is 'auto' which uses 1 / n_features, if ``gamma='scale'`` is passed then it uses 1 / (n_features * X.std()) as value of gamma. The current default of gamma, 'auto', will change to 'scale' in version 0.22. 'auto_deprecated', a deprecated version of 'auto' is used as a default indicating that no explicit value of gamma was passed | default: "auto" |

kernel | Specifies the kernel type to be used in the algorithm It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or a callable If none is given, 'rbf' will be used. If a callable is given it is used to pre-compute the kernel matrix from data matrices; that matrix should be an array of shape ``(n_samples, n_samples)`` | default: "linear" |

max_iter | Hard limit on iterations within solver, or -1 for no limit | default: -1 |

probability | Whether to enable probability estimates. This must be enabled prior to calling `fit`, and will slow down that method | default: true |

random_state | The seed of the pseudo random number generator used when shuffling the data for probability estimates. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. | default: null |

shrinking | Whether to use the shrinking heuristic | default: true |

tol | Tolerance for stopping criterion | default: 1e-05 |

verbose | Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in libsvm that, if enabled, may not work properly in a multithreaded context | default: false |

0

Runs
Parameter:

Rendering chart

Rendering table