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| { | |
| "sklearn_version": "1.4.2", | |
| "models": { | |
| "logistic_regression": [ | |
| { | |
| "name": "penalty", | |
| "label": "Penalty", | |
| "description": "Specify the norm of the penalty:", | |
| "kind": "mixed", | |
| "options": [ | |
| { | |
| "value": "elasticnet", | |
| "label": "elasticnet" | |
| }, | |
| { | |
| "value": "l1", | |
| "label": "l1" | |
| }, | |
| { | |
| "value": "l2", | |
| "label": "l2" | |
| }, | |
| { | |
| "value": null, | |
| "label": "None" | |
| } | |
| ], | |
| "default": "l2" | |
| }, | |
| { | |
| "name": "dual", | |
| "label": "Dual", | |
| "description": "Dual (constrained) or primal (regularized, see also this equation) formulation.", | |
| "kind": "boolean", | |
| "default": false | |
| }, | |
| { | |
| "name": "tol", | |
| "label": "Tol", | |
| "description": "Tolerance for stopping criteria.", | |
| "kind": "number", | |
| "min": 0, | |
| "default": 0.0001 | |
| }, | |
| { | |
| "name": "C", | |
| "label": "C", | |
| "description": "Inverse of regularization strength; must be a positive float.", | |
| "kind": "number", | |
| "min": 0, | |
| "minExclusive": true, | |
| "default": 1.0 | |
| }, | |
| { | |
| "name": "fit_intercept", | |
| "label": "Fit intercept", | |
| "description": "Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function.", | |
| "kind": "boolean", | |
| "default": true | |
| }, | |
| { | |
| "name": "intercept_scaling", | |
| "label": "Intercept scaling", | |
| "description": "Useful only when the solver 'liblinear' is used and self.fit_intercept is set to True.", | |
| "kind": "number", | |
| "min": 0, | |
| "minExclusive": true, | |
| "default": 1 | |
| }, | |
| { | |
| "name": "class_weight", | |
| "label": "Class weight", | |
| "description": "Weights associated with classes in the form {class_label: weight}.", | |
| "kind": "mixed", | |
| "options": [ | |
| { | |
| "value": "balanced", | |
| "label": "balanced" | |
| }, | |
| { | |
| "value": null, | |
| "label": "None" | |
| } | |
| ], | |
| "default": null | |
| }, | |
| { | |
| "name": "random_state", | |
| "label": "Random state", | |
| "description": "Used when solver == 'sag', 'saga' or 'liblinear' to shuffle the data.", | |
| "kind": "integer", | |
| "min": 0, | |
| "max": 4294967295, | |
| "nullable": "None", | |
| "default": null | |
| }, | |
| { | |
| "name": "solver", | |
| "label": "Solver", | |
| "description": "Algorithm to use in the optimization problem.", | |
| "kind": "choice", | |
| "options": [ | |
| { | |
| "value": "lbfgs", | |
| "label": "lbfgs" | |
| }, | |
| { | |
| "value": "liblinear", | |
| "label": "liblinear" | |
| }, | |
| { | |
| "value": "newton-cg", | |
| "label": "newton-cg" | |
| }, | |
| { | |
| "value": "newton-cholesky", | |
| "label": "newton-cholesky" | |
| }, | |
| { | |
| "value": "sag", | |
| "label": "sag" | |
| }, | |
| { | |
| "value": "saga", | |
| "label": "saga" | |
| } | |
| ], | |
| "default": "lbfgs" | |
| }, | |
| { | |
| "name": "max_iter", | |
| "label": "Max iter", | |
| "description": "Maximum number of iterations taken for the solvers to converge.", | |
| "kind": "integer", | |
| "min": 0, | |
| "default": 100 | |
| }, | |
| { | |
| "name": "l1_ratio", | |
| "label": "L1 ratio", | |
| "description": "The Elastic-Net mixing parameter, with 0 <= l1_ratio <= 1.", | |
| "kind": "number", | |
| "min": 0, | |
| "max": 1, | |
| "nullable": "None", | |
| "default": null | |
| } | |
| ], | |
| "knn": [ | |
| { | |
| "name": "n_neighbors", | |
| "label": "N neighbors", | |
| "description": "Number of neighbors to use by default for kneighbors queries.", | |
| "kind": "integer", | |
| "min": 1, | |
| "nullable": "None", | |
| "default": 5 | |
| }, | |
| { | |
| "name": "algorithm", | |
| "label": "Algorithm", | |
| "description": "Algorithm used to compute the nearest neighbors:", | |
| "kind": "choice", | |
| "options": [ | |
| { | |
| "value": "auto", | |
| "label": "auto" | |
| }, | |
| { | |
| "value": "ball_tree", | |
| "label": "ball_tree" | |
| }, | |
| { | |
| "value": "brute", | |
| "label": "brute" | |
| }, | |
| { | |
| "value": "kd_tree", | |
| "label": "kd_tree" | |
| } | |
| ], | |
| "default": "auto" | |
| }, | |
| { | |
| "name": "leaf_size", | |
| "label": "Leaf size", | |
| "description": "Leaf size passed to BallTree or KDTree.", | |
| "kind": "integer", | |
| "min": 1, | |
| "default": 30 | |
| }, | |
| { | |
| "name": "p", | |
| "label": "p", | |
| "description": "Power parameter for the Minkowski metric.", | |
| "kind": "number", | |
| "min": 0, | |
| "minExclusive": true, | |
| "nullable": "None", | |
| "default": 2 | |
| }, | |
| { | |
| "name": "metric", | |
| "label": "Metric", | |
| "description": "Metric to use for distance computation.", | |
| "kind": "choice", | |
| "options": [ | |
| { | |
| "value": "braycurtis", | |
| "label": "braycurtis" | |
| }, | |
| { | |
| "value": "canberra", | |
| "label": "canberra" | |
| }, | |
| { | |
| "value": "chebyshev", | |
| "label": "chebyshev" | |
| }, | |
| { | |
| "value": "cityblock", | |
| "label": "cityblock" | |
| }, | |
| { | |
| "value": "correlation", | |
| "label": "correlation" | |
| }, | |
| { | |
| "value": "cosine", | |
| "label": "cosine" | |
| }, | |
| { | |
| "value": "dice", | |
| "label": "dice" | |
| }, | |
| { | |
| "value": "euclidean", | |
| "label": "euclidean" | |
| }, | |
| { | |
| "value": "hamming", | |
| "label": "hamming" | |
| }, | |
| { | |
| "value": "haversine", | |
| "label": "haversine" | |
| }, | |
| { | |
| "value": "infinity", | |
| "label": "infinity" | |
| }, | |
| { | |
| "value": "jaccard", | |
| "label": "jaccard" | |
| }, | |
| { | |
| "value": "l1", | |
| "label": "l1" | |
| }, | |
| { | |
| "value": "l2", | |
| "label": "l2" | |
| }, | |
| { | |
| "value": "mahalanobis", | |
| "label": "mahalanobis" | |
| }, | |
| { | |
| "value": "manhattan", | |
| "label": "manhattan" | |
| }, | |
| { | |
| "value": "minkowski", | |
| "label": "minkowski" | |
| }, | |
| { | |
| "value": "nan_euclidean", | |
| "label": "nan_euclidean" | |
| }, | |
| { | |
| "value": "p", | |
| "label": "p" | |
| }, | |
| { | |
| "value": "pyfunc", | |
| "label": "pyfunc" | |
| }, | |
| { | |
| "value": "rogerstanimoto", | |
| "label": "rogerstanimoto" | |
| }, | |
| { | |
| "value": "russellrao", | |
| "label": "russellrao" | |
| }, | |
| { | |
| "value": "seuclidean", | |
| "label": "seuclidean" | |
| }, | |
| { | |
| "value": "sokalmichener", | |
| "label": "sokalmichener" | |
| }, | |
| { | |
| "value": "sokalsneath", | |
| "label": "sokalsneath" | |
| }, | |
| { | |
| "value": "sqeuclidean", | |
| "label": "sqeuclidean" | |
| }, | |
| { | |
| "value": "yule", | |
| "label": "yule" | |
| } | |
| ], | |
| "default": "minkowski" | |
| }, | |
| { | |
| "name": "weights", | |
| "label": "Weights", | |
| "description": "Weight function used in prediction.", | |
| "kind": "mixed", | |
| "options": [ | |
| { | |
| "value": "distance", | |
| "label": "distance" | |
| }, | |
| { | |
| "value": "uniform", | |
| "label": "uniform" | |
| }, | |
| { | |
| "value": null, | |
| "label": "None" | |
| } | |
| ], | |
| "default": "uniform" | |
| } | |
| ], | |
| "svm": [ | |
| { | |
| "name": "kernel", | |
| "label": "Kernel", | |
| "description": "Specifies the kernel type to be used in the algorithm.", | |
| "kind": "choice", | |
| "options": [ | |
| { | |
| "value": "linear", | |
| "label": "linear" | |
| }, | |
| { | |
| "value": "poly", | |
| "label": "poly" | |
| }, | |
| { | |
| "value": "rbf", | |
| "label": "rbf" | |
| }, | |
| { | |
| "value": "sigmoid", | |
| "label": "sigmoid" | |
| } | |
| ], | |
| "default": "rbf" | |
| }, | |
| { | |
| "name": "degree", | |
| "label": "Degree", | |
| "description": "Degree of the polynomial kernel function ('poly').", | |
| "kind": "integer", | |
| "min": 0, | |
| "default": 3 | |
| }, | |
| { | |
| "name": "gamma", | |
| "label": "Gamma", | |
| "description": "Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.", | |
| "kind": "mixed", | |
| "options": [ | |
| { | |
| "value": "auto", | |
| "label": "auto" | |
| }, | |
| { | |
| "value": "scale", | |
| "label": "scale" | |
| } | |
| ], | |
| "number": { | |
| "integer": false, | |
| "min": 0.0 | |
| }, | |
| "default": "scale" | |
| }, | |
| { | |
| "name": "coef0", | |
| "label": "Coef0", | |
| "description": "Independent term in kernel function.", | |
| "kind": "number", | |
| "default": 0.0 | |
| }, | |
| { | |
| "name": "tol", | |
| "label": "Tol", | |
| "description": "Tolerance for stopping criterion.", | |
| "kind": "number", | |
| "min": 0.0, | |
| "minExclusive": true, | |
| "default": 0.001 | |
| }, | |
| { | |
| "name": "C", | |
| "label": "C", | |
| "description": "Regularization parameter.", | |
| "kind": "number", | |
| "min": 0.0, | |
| "minExclusive": true, | |
| "default": 1.0 | |
| }, | |
| { | |
| "name": "shrinking", | |
| "label": "Shrinking", | |
| "description": "Whether to use the shrinking heuristic.", | |
| "kind": "boolean", | |
| "default": true | |
| }, | |
| { | |
| "name": "probability", | |
| "label": "Probability", | |
| "description": "Whether to enable probability estimates.", | |
| "kind": "boolean", | |
| "default": false | |
| }, | |
| { | |
| "name": "cache_size", | |
| "label": "Cache size", | |
| "description": "Specify the size of the kernel cache (in MB).", | |
| "kind": "number", | |
| "min": 0, | |
| "minExclusive": true, | |
| "default": 200 | |
| }, | |
| { | |
| "name": "class_weight", | |
| "label": "Class weight", | |
| "description": "Set the parameter C of class i to class_weight[i]*C for SVC.", | |
| "kind": "mixed", | |
| "options": [ | |
| { | |
| "value": "balanced", | |
| "label": "balanced" | |
| }, | |
| { | |
| "value": null, | |
| "label": "None" | |
| } | |
| ], | |
| "default": null | |
| }, | |
| { | |
| "name": "max_iter", | |
| "label": "Max iter", | |
| "description": "Hard limit on iterations within solver, or -1 for no limit.", | |
| "kind": "integer", | |
| "min": -1, | |
| "default": -1 | |
| }, | |
| { | |
| "name": "random_state", | |
| "label": "Random state", | |
| "description": "Controls the pseudo random number generation for shuffling the data for probability estimates.", | |
| "kind": "integer", | |
| "min": 0, | |
| "max": 4294967295, | |
| "nullable": "None", | |
| "default": null | |
| }, | |
| { | |
| "name": "decision_function_shape", | |
| "label": "Decision function shape", | |
| "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).", | |
| "kind": "choice", | |
| "options": [ | |
| { | |
| "value": "ovo", | |
| "label": "ovo" | |
| }, | |
| { | |
| "value": "ovr", | |
| "label": "ovr" | |
| } | |
| ], | |
| "default": "ovr" | |
| }, | |
| { | |
| "name": "break_ties", | |
| "label": "Break ties", | |
| "description": "If true, decision_function_shape='ovr', and number of classes > 2, predict will break ties according to the confidence values of decision_function; otherwise the first class among the tied classes is returned.", | |
| "kind": "boolean", | |
| "default": false | |
| } | |
| ], | |
| "decision_tree": [ | |
| { | |
| "name": "splitter", | |
| "label": "Splitter", | |
| "description": "The strategy used to choose the split at each node.", | |
| "kind": "choice", | |
| "options": [ | |
| { | |
| "value": "best", | |
| "label": "best" | |
| }, | |
| { | |
| "value": "random", | |
| "label": "random" | |
| } | |
| ], | |
| "default": "best" | |
| }, | |
| { | |
| "name": "max_depth", | |
| "label": "Max depth", | |
| "description": "The maximum depth of the tree.", | |
| "kind": "integer", | |
| "min": 1, | |
| "nullable": "None", | |
| "default": null | |
| }, | |
| { | |
| "name": "min_samples_split", | |
| "label": "Min samples split", | |
| "description": "The minimum number of samples required to split an internal node:", | |
| "kind": "number", | |
| "min": 0.0, | |
| "minExclusive": true, | |
| "default": 2 | |
| }, | |
| { | |
| "name": "min_samples_leaf", | |
| "label": "Min samples leaf", | |
| "description": "The minimum number of samples required to be at a leaf node.", | |
| "kind": "number", | |
| "min": 0.0, | |
| "minExclusive": true, | |
| "default": 1 | |
| }, | |
| { | |
| "name": "min_weight_fraction_leaf", | |
| "label": "Min weight fraction leaf", | |
| "description": "The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node.", | |
| "kind": "number", | |
| "min": 0.0, | |
| "max": 0.5, | |
| "default": 0.0 | |
| }, | |
| { | |
| "name": "max_features", | |
| "label": "Max features", | |
| "description": "The number of features to consider when looking for the best split:", | |
| "kind": "mixed", | |
| "options": [ | |
| { | |
| "value": "log2", | |
| "label": "log2" | |
| }, | |
| { | |
| "value": "sqrt", | |
| "label": "sqrt" | |
| }, | |
| { | |
| "value": null, | |
| "label": "None" | |
| } | |
| ], | |
| "number": { | |
| "integer": false, | |
| "min": 0.0, | |
| "minExclusive": true | |
| }, | |
| "default": null | |
| }, | |
| { | |
| "name": "random_state", | |
| "label": "Random state", | |
| "description": "Controls the randomness of the estimator.", | |
| "kind": "integer", | |
| "min": 0, | |
| "max": 4294967295, | |
| "nullable": "None", | |
| "default": null | |
| }, | |
| { | |
| "name": "max_leaf_nodes", | |
| "label": "Max leaf nodes", | |
| "description": "Grow a tree with max_leaf_nodes in best-first fashion.", | |
| "kind": "integer", | |
| "min": 2, | |
| "nullable": "None", | |
| "default": null | |
| }, | |
| { | |
| "name": "min_impurity_decrease", | |
| "label": "Min impurity decrease", | |
| "description": "A node will be split if this split induces a decrease of the impurity greater than or equal to this value.", | |
| "kind": "number", | |
| "min": 0.0, | |
| "default": 0.0 | |
| }, | |
| { | |
| "name": "ccp_alpha", | |
| "label": "Ccp alpha", | |
| "description": "Complexity parameter used for Minimal Cost-Complexity Pruning.", | |
| "kind": "number", | |
| "min": 0.0, | |
| "default": 0.0 | |
| }, | |
| { | |
| "name": "criterion", | |
| "label": "Criterion", | |
| "description": "The function to measure the quality of a split.", | |
| "kind": "choice", | |
| "options": [ | |
| { | |
| "value": "entropy", | |
| "label": "entropy" | |
| }, | |
| { | |
| "value": "gini", | |
| "label": "gini" | |
| }, | |
| { | |
| "value": "log_loss", | |
| "label": "log_loss" | |
| } | |
| ], | |
| "default": "gini" | |
| }, | |
| { | |
| "name": "class_weight", | |
| "label": "Class weight", | |
| "description": "Weights associated with classes in the form {class_label: weight}.", | |
| "kind": "mixed", | |
| "options": [ | |
| { | |
| "value": "balanced", | |
| "label": "balanced" | |
| }, | |
| { | |
| "value": null, | |
| "label": "None" | |
| } | |
| ], | |
| "default": null | |
| } | |
| ], | |
| "random_forest": [ | |
| { | |
| "name": "n_estimators", | |
| "label": "N estimators", | |
| "description": "The number of trees in the forest.", | |
| "kind": "integer", | |
| "min": 1, | |
| "default": 100 | |
| }, | |
| { | |
| "name": "bootstrap", | |
| "label": "Bootstrap", | |
| "description": "Whether bootstrap samples are used when building trees.", | |
| "kind": "boolean", | |
| "default": true | |
| }, | |
| { | |
| "name": "oob_score", | |
| "label": "Oob score", | |
| "description": "Whether to use out-of-bag samples to estimate the generalization score.", | |
| "kind": "boolean", | |
| "default": false | |
| }, | |
| { | |
| "name": "random_state", | |
| "label": "Random state", | |
| "description": "Controls both the randomness of the bootstrapping of the samples used when building trees (if bootstrap=True) and the sampling of the features to consider when looking for the best split at each node (if max_features < n_features).", | |
| "kind": "integer", | |
| "min": 0, | |
| "max": 4294967295, | |
| "nullable": "None", | |
| "default": null | |
| }, | |
| { | |
| "name": "max_samples", | |
| "label": "Max samples", | |
| "description": "If bootstrap is True, the number of samples to draw from X to train each base estimator.", | |
| "kind": "number", | |
| "min": 0.0, | |
| "minExclusive": true, | |
| "nullable": "None", | |
| "default": null | |
| }, | |
| { | |
| "name": "max_depth", | |
| "label": "Max depth", | |
| "description": "The maximum depth of the tree.", | |
| "kind": "integer", | |
| "min": 1, | |
| "nullable": "None", | |
| "default": null | |
| }, | |
| { | |
| "name": "min_samples_split", | |
| "label": "Min samples split", | |
| "description": "The minimum number of samples required to split an internal node:", | |
| "kind": "number", | |
| "min": 0.0, | |
| "minExclusive": true, | |
| "default": 2 | |
| }, | |
| { | |
| "name": "min_samples_leaf", | |
| "label": "Min samples leaf", | |
| "description": "The minimum number of samples required to be at a leaf node.", | |
| "kind": "number", | |
| "min": 0.0, | |
| "minExclusive": true, | |
| "default": 1 | |
| }, | |
| { | |
| "name": "min_weight_fraction_leaf", | |
| "label": "Min weight fraction leaf", | |
| "description": "The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node.", | |
| "kind": "number", | |
| "min": 0.0, | |
| "max": 0.5, | |
| "default": 0.0 | |
| }, | |
| { | |
| "name": "max_features", | |
| "label": "Max features", | |
| "description": "The number of features to consider when looking for the best split:", | |
| "kind": "mixed", | |
| "options": [ | |
| { | |
| "value": "log2", | |
| "label": "log2" | |
| }, | |
| { | |
| "value": "sqrt", | |
| "label": "sqrt" | |
| }, | |
| { | |
| "value": null, | |
| "label": "None" | |
| } | |
| ], | |
| "number": { | |
| "integer": false, | |
| "min": 0.0, | |
| "minExclusive": true | |
| }, | |
| "default": "sqrt" | |
| }, | |
| { | |
| "name": "max_leaf_nodes", | |
| "label": "Max leaf nodes", | |
| "description": "Grow trees with max_leaf_nodes in best-first fashion.", | |
| "kind": "integer", | |
| "min": 2, | |
| "nullable": "None", | |
| "default": null | |
| }, | |
| { | |
| "name": "min_impurity_decrease", | |
| "label": "Min impurity decrease", | |
| "description": "A node will be split if this split induces a decrease of the impurity greater than or equal to this value.", | |
| "kind": "number", | |
| "min": 0.0, | |
| "default": 0.0 | |
| }, | |
| { | |
| "name": "ccp_alpha", | |
| "label": "Ccp alpha", | |
| "description": "Complexity parameter used for Minimal Cost-Complexity Pruning.", | |
| "kind": "number", | |
| "min": 0.0, | |
| "default": 0.0 | |
| }, | |
| { | |
| "name": "criterion", | |
| "label": "Criterion", | |
| "description": "The function to measure the quality of a split.", | |
| "kind": "choice", | |
| "options": [ | |
| { | |
| "value": "entropy", | |
| "label": "entropy" | |
| }, | |
| { | |
| "value": "gini", | |
| "label": "gini" | |
| }, | |
| { | |
| "value": "log_loss", | |
| "label": "log_loss" | |
| } | |
| ], | |
| "default": "gini" | |
| }, | |
| { | |
| "name": "class_weight", | |
| "label": "Class weight", | |
| "description": "Weights associated with classes in the form {class_label: weight}.", | |
| "kind": "mixed", | |
| "options": [ | |
| { | |
| "value": "balanced", | |
| "label": "balanced" | |
| }, | |
| { | |
| "value": "balanced_subsample", | |
| "label": "balanced_subsample" | |
| }, | |
| { | |
| "value": null, | |
| "label": "None" | |
| } | |
| ], | |
| "default": null | |
| } | |
| ], | |
| "gradient_boosting": [ | |
| { | |
| "name": "max_depth", | |
| "label": "Max depth", | |
| "description": "Maximum depth of the individual regression estimators.", | |
| "kind": "integer", | |
| "min": 1, | |
| "nullable": "None", | |
| "default": 3 | |
| }, | |
| { | |
| "name": "min_samples_split", | |
| "label": "Min samples split", | |
| "description": "The minimum number of samples required to split an internal node:", | |
| "kind": "number", | |
| "min": 0.0, | |
| "minExclusive": true, | |
| "default": 2 | |
| }, | |
| { | |
| "name": "min_samples_leaf", | |
| "label": "Min samples leaf", | |
| "description": "The minimum number of samples required to be at a leaf node.", | |
| "kind": "number", | |
| "min": 0.0, | |
| "minExclusive": true, | |
| "default": 1 | |
| }, | |
| { | |
| "name": "min_weight_fraction_leaf", | |
| "label": "Min weight fraction leaf", | |
| "description": "The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node.", | |
| "kind": "number", | |
| "min": 0.0, | |
| "max": 0.5, | |
| "default": 0.0 | |
| }, | |
| { | |
| "name": "max_features", | |
| "label": "Max features", | |
| "description": "The number of features to consider when looking for the best split:", | |
| "kind": "mixed", | |
| "options": [ | |
| { | |
| "value": "log2", | |
| "label": "log2" | |
| }, | |
| { | |
| "value": "sqrt", | |
| "label": "sqrt" | |
| }, | |
| { | |
| "value": null, | |
| "label": "None" | |
| } | |
| ], | |
| "number": { | |
| "integer": false, | |
| "min": 0.0, | |
| "minExclusive": true | |
| }, | |
| "default": null | |
| }, | |
| { | |
| "name": "random_state", | |
| "label": "Random state", | |
| "description": "Controls the random seed given to each Tree estimator at each boosting iteration.", | |
| "kind": "integer", | |
| "min": 0, | |
| "max": 4294967295, | |
| "nullable": "None", | |
| "default": null | |
| }, | |
| { | |
| "name": "max_leaf_nodes", | |
| "label": "Max leaf nodes", | |
| "description": "Grow trees with max_leaf_nodes in best-first fashion.", | |
| "kind": "integer", | |
| "min": 2, | |
| "nullable": "None", | |
| "default": null | |
| }, | |
| { | |
| "name": "min_impurity_decrease", | |
| "label": "Min impurity decrease", | |
| "description": "A node will be split if this split induces a decrease of the impurity greater than or equal to this value.", | |
| "kind": "number", | |
| "min": 0.0, | |
| "default": 0.0 | |
| }, | |
| { | |
| "name": "ccp_alpha", | |
| "label": "Ccp alpha", | |
| "description": "Complexity parameter used for Minimal Cost-Complexity Pruning.", | |
| "kind": "number", | |
| "min": 0.0, | |
| "default": 0.0 | |
| }, | |
| { | |
| "name": "criterion", | |
| "label": "Criterion", | |
| "description": "The function to measure the quality of a split.", | |
| "kind": "choice", | |
| "options": [ | |
| { | |
| "value": "friedman_mse", | |
| "label": "friedman_mse" | |
| }, | |
| { | |
| "value": "squared_error", | |
| "label": "squared_error" | |
| } | |
| ], | |
| "default": "friedman_mse" | |
| }, | |
| { | |
| "name": "learning_rate", | |
| "label": "Learning rate", | |
| "description": "Learning rate shrinks the contribution of each tree by learning_rate.", | |
| "kind": "number", | |
| "min": 0.0, | |
| "default": 0.1 | |
| }, | |
| { | |
| "name": "n_estimators", | |
| "label": "N estimators", | |
| "description": "The number of boosting stages to perform.", | |
| "kind": "integer", | |
| "min": 1, | |
| "default": 100 | |
| }, | |
| { | |
| "name": "subsample", | |
| "label": "Subsample", | |
| "description": "The fraction of samples to be used for fitting the individual base learners.", | |
| "kind": "number", | |
| "min": 0.0, | |
| "minExclusive": true, | |
| "max": 1.0, | |
| "default": 1.0 | |
| }, | |
| { | |
| "name": "validation_fraction", | |
| "label": "Validation fraction", | |
| "description": "The proportion of training data to set aside as validation set for early stopping.", | |
| "kind": "number", | |
| "min": 0.0, | |
| "minExclusive": true, | |
| "max": 1.0, | |
| "maxExclusive": true, | |
| "default": 0.1 | |
| }, | |
| { | |
| "name": "n_iter_no_change", | |
| "label": "N iter no change", | |
| "description": "n_iter_no_change is used to decide if early stopping will be used to terminate training when validation score is not improving.", | |
| "kind": "integer", | |
| "min": 1, | |
| "nullable": "None", | |
| "default": null | |
| }, | |
| { | |
| "name": "tol", | |
| "label": "Tol", | |
| "description": "Tolerance for the early stopping.", | |
| "kind": "number", | |
| "min": 0.0, | |
| "default": 0.0001 | |
| }, | |
| { | |
| "name": "loss", | |
| "label": "Loss", | |
| "description": "The loss function to be optimized. 'log_loss' refers to binomial and multinomial deviance, the same as used in logistic regression.", | |
| "kind": "choice", | |
| "options": [ | |
| { | |
| "value": "exponential", | |
| "label": "exponential" | |
| }, | |
| { | |
| "value": "log_loss", | |
| "label": "log_loss" | |
| } | |
| ], | |
| "default": "log_loss" | |
| }, | |
| { | |
| "name": "init", | |
| "label": "Init", | |
| "description": "An estimator object that is used to compute the initial predictions. init has to provide fit and predict_proba.", | |
| "kind": "mixed", | |
| "options": [ | |
| { | |
| "value": "zero", | |
| "label": "zero" | |
| }, | |
| { | |
| "value": null, | |
| "label": "None" | |
| } | |
| ], | |
| "default": null | |
| } | |
| ] | |
| } | |
| } | |