| Issue | #Downvotes for this reason | By |
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| C | Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive The penalty is a squared l2 penalty | default: 1.0 |
| cache_size | Specify the size of the kernel cache (in MB) | default: 200 |
| coef0 | Independent term in kernel function It is only significant in 'poly' and 'sigmoid' | default: 0.0 |
| degree | Degree of the polynomial kernel function ('poly') Ignored by all other kernels gamma : {'scale', 'auto'} or float, default='scale' Kernel coefficient for 'rbf', 'poly' and 'sigmoid' - if ``gamma='scale'`` (default) is passed then it uses 1 / (n_features * X.var()) as value of gamma, - if 'auto', uses 1 / n_features .. versionchanged:: 0.22 The default value of ``gamma`` changed from 'auto' to 'scale' | default: 3 |
| epsilon | Epsilon in the epsilon-SVR model. It specifies the epsilon-tube within which no penalty is associated in the training loss function with points predicted within a distance epsilon from the actual value | default: 0.1 |
| gamma | default: "scale" | |
| kernel | default: "rbf" | |
| max_iter | Hard limit on iterations within solver, or -1 for no limit. | default: -1 |
| shrinking | Whether to use the shrinking heuristic
See the :ref:`User Guide | default: true |
| tol | Tolerance for stopping criterion | default: 0.001 |
| 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 |