@gracexwho/model-card-generator
Version:
Tool for generating model cards for Jupyter Notebook.
104 lines • 5.57 kB
JSON
{"allOf": [
{ "type": "object",
"additionalProperties": "False",
"required": ["kernel", "degree", "gamma", "shrinking", "tol", "cache_size", "max_iter", "decision_function_shape"],
"relevantToOptimizer": ["kernel", "degree", "gamma", "shrinking", "probability", "tol"],
"properties": {
"C": {
"description": "Penalty parameter C of the error term.",
"type": "number",
"distribution": "loguniform",
"minimum": 0.0,
"exclusiveMinimum": "True",
"default": 1.0,
"minimumForOptimizer": 0.03125,
"maximumForOptimizer": 32768},
"kernel": {
"anyOf": [
{ "enum":["precomputed"], "forOptimizer": "False"},
{ "enum": ["linear", "poly", "rbf", "sigmoid"]},
{ "laleType": "callable", "forOptimizer": "False"}],
"default": "rbf",
"description":
"Specifies the kernel type to be used in the algorithm."},
"degree": {
"type": "integer",
"minimum": 0,
"minimumForOptimizer": 2,
"maximumForOptimizer": 5,
"default": 3,
"description": "Degree of the polynomial kernel function ('poly')."},
"gamma": {
"anyOf": [
{ "type": "number",
"minimum": 0.0,
"exclusiveMinimum": "True",
"minimumForOptimizer": 3.0517578125e-05,
"maximumForOptimizer": 8,
"distribution": "loguniform"},
{ "enum": ["auto", "auto_deprecated", "scale"]}],
"default": "auto_deprecated",
"description": "Kernel coefficient for 'rbf', 'poly', and 'sigmoid'."},
"coef0": {
"type": "number",
"default": 0.0,
"description": "Independent term in kernel function."},
"shrinking": {
"type": "boolean",
"default": "True",
"description": "Whether to use the shrinking heuristic."},
"probability": {
"type": "boolean",
"default": "False",
"description": "Whether to enable probability estimates."},
"tol": {
"type": "number",
"distribution": "loguniform",
"minimum": 0.0,
"exclusiveMinimum": "True",
"maximumForOptimizer": 0.01,
"default": 0.0001,
"description": "Tolerance for stopping criteria."},
"cache_size": {
"type": "integer",
"default": 200,
"description": "Specify the size of the kernel cache (in MB)."},
"class_weight": {
"anyOf": [
{ "description": "By default, all classes have weight 1.",
"enum": [null]},
{ "description": "Adjust weights by inverse frequency.",
"enum": ["balanced"]},
{ "description": "Dictionary mapping class labels to weights.",
"type": "object",
"additionalProperties": {"type": "number"},
"forOptimizer": "False"}],
"default": null},
"verbose": {
"type": "boolean",
"default": "False",
"description": "Enable verbose output."},
"max_iter": {
"type": "integer",
"default": -1,
"description": "Hard limit on iterations within solver, or -1 for no limit."},
"decision_function_shape": {
"enum": ["ovo", "ovr"],
"default": "ovr",
"description": "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)."},
"random_state": {
"description":
"Seed of pseudo-random number generator.",
"anyOf": [
{ "laleType": "numpy.random.RandomState"},
{ "description": "RandomState used by np.random",
"enum": [null]},
{ "description": "Explicit seed.",
"type": "integer"}],
"default": null}}},
{ "description": "coef0 only significant in kernel ‘poly’ and ‘sigmoid’.",
"anyOf": [
{ "type": "object",
"properties": {"kernel": {"enum": ["poly", "sigmoid"]}}},
{ "type": "object",
"properties": {"coef0": {"enum": [0.0]}}}]}]}