forked from wangtao/SoftwareEngineeringCase
1092 lines
35 KiB
JSON
1092 lines
35 KiB
JSON
{
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"description":"",
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"ClassificationEstimator":{
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"逻辑回归": "LogisticRegression",
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"支持向量分类": "SVC",
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"多层感知器分类": "MLPClassifier",
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"决策树分类": "DecisionTreeClassifier",
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"随机森林分类": "RandomForestClassifier",
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"梯度提升树分类": "GradientBoostingClassifier"
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},
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"ClusterEstimator":{
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"K-Means聚类": "KMeans"
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},
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"RegressionEstimator":{
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"线性回归":"LinearRegression",
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"岭回归": "Ridge",
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"套索回归": "Lasso",
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"支持向量回归": "SVR",
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"多层感知器回归": "MLPRegressor",
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"决策树回归": "DecisionTreeRegressor",
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"随机森林回归": "RandomForestRegressor",
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"梯度提升树回归": "GradientBoostingRegressor"
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},
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"LogisticRegression":{
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"name":"逻辑回归",
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"parameters":{
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"default parameters":{
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"description":"full",
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"penalty":{
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"name":"正则化",
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"value":"l2",
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"type":["string"]
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},
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"C":{
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"name":"正则化强度",
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"value":1.0,
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"type":["float", "list"]
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},
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"solver":{
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"name":"优化算法",
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"value":"liblinear",
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"type":["string"]
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},
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"max_iter":{
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"name":"迭代次数",
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"value":100,
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"type":["int"]
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},
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"multi_class":{
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"name":"多分类方式",
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"value":"ovr",
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"type":["string"]
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}
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},
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"tuning parameters":{
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"description":"full",
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"penalty":{
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"name":"正则化",
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"value":["l1", "l2", "elasticnet", "none"],
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"type":["string"]
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},
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"C":{
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"name":"正则化强度",
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"value":[1.0, 2.0, 10.0],
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"type":["float", "list"]
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},
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"solver":{
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"name":"优化算法",
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"value":["liblinear"],
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"type":["string"]
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},
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"max_iter":{
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"name":"迭代次数",
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"value":[50, 100, 500],
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"type":["int"]
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},
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"multi_class":{
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"name":"多分类方式",
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"value":["ovr", "multinomial", "auto"],
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"type":["string"]
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}
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}
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}
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},
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"SVC":{
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"name":"支持向量分类",
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"parameters":{
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"default parameters":{
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"description":"full",
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"kernel":{
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"name":"核函数",
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"value":"rbf",
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"type":["string"]
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},
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"degree":{
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"name":"核函数阶数",
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"value":3,
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"type":["int"]
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},
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"gamma":{
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"name":"核函数系数",
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"value":"auto",
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"type":["string"]
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},
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"coef0":{
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"name":"核函数常数项",
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"value":0.0,
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"type":["float"]
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},
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"tol":{
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"name":"容忍停止标准",
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"value":1e-3,
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"type":["float"]
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},
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"C":{
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"name":"误差项惩罚系数",
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"value":1.0,
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"type":["int"]
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},
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"max_iter":{
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"name":"迭代次数",
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"value":-1,
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"type":["int"]
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}
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},
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"tuning parameters":{
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"description":"full",
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"kernel":{
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"name":"核函数",
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"value":["linear", "poly", "rbf", "sigmoid"],
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"type":["string"]
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},
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"degree":{
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"name":"核函数阶数",
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"value":[3, 7, 10],
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"type":["int"]
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},
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"gamma":{
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"name":"核函数系数",
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"value":["scale", "auto"],
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"type":["string"]
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},
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"coef0":{
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"name":"核函数常数项",
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"value":[0.0, 0.05, 0.1, 0.9],
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"type":["float"]
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},
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"tol":{
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"name":"容忍停止标准",
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"value":[1e-3, 1e-2],
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"type":["float"]
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},
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"C":{
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"name":"误差项惩罚系数",
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"value":[1.0, 2.0, 10.0],
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"type":["int"]
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},
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"max_iter":{
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"name":"迭代次数",
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"value":[-1],
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"type":["int"]
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}
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}
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}
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},
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"MLPClassifier":{
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"name":"多层感知器分类",
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"parameters":{
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"default parameters":{
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"description":"full",
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"hidden_layer_sizes":{
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"name":"网络结构",
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"value":100,
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"type":["tuple"]
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},
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"activation":{
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"name":"激活函数",
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"value":"relu",
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"type":["string"]
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},
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"solver":{
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"name":"优化算法",
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"value":"adam",
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"type":["string"]
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},
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"alpha":{
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"name":"L2惩罚项系数",
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"value":1e-4,
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"type":["float"]
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},
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"batch_size":{
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"name":"批次大小",
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"value":["auto"],
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"type":["int", "string"]
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},
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"max_iter":{
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"name":"最大迭代次数",
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"value":200,
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"type":["double"]
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},
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"learning_rate_init":{
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"name":"初始学习率",
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"value":0.001,
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"type":["double"]
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}
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},
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"tuning parameters":{
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"description":"full",
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"hidden_layer_sizes":{
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"name":"网络结构",
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"value":[100, 10],
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"type":["tuple"]
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},
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"activation":{
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"name":"激活函数",
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"value":["identity", "logistic", "tanh", "relu"],
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"type":["string"]
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},
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"solver":{
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"name":"优化算法",
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"value":["lbfgs", "sgd"],
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"type":["string"]
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},
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"alpha":{
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"name":"L2惩罚项系数",
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"value":[1e-4, 5e-3],
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"type":["float"]
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},
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"batch_size":{
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"name":"批次大小",
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"value":["auto"],
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"type":["int", "string"]
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},
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"max_iter":{
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"name":"最大迭代次数",
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"value":[5, 20, 50, 100],
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"type":["double"]
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},
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"learning_rate_init":{
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"name":"初始学习率",
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"value":[0.001, 0.0001],
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"type":["double"]
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}
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}
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}
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},
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"DecisionTreeClassifier":{
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"name":"决策树分类",
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"parameters":{
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"default parameters":{
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"description":"full",
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"criterion":{
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"name":"节点分裂放方式",
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"value":"gini",
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"type":["string"]
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},
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"splitter":{
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"name":"分裂节点选择策略",
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"value":"best",
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"type":["string"]
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},
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"max_depth":{
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"name":"最大深度",
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"value":"None",
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"type":["int", "None"]
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},
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"max_features":{
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"name":"节点分裂最大特征数",
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"value":"None",
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"type":["string"]
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},
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"min_samples_split":{
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"name":"节点分裂最小样本数",
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"value":2,
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"type":["int", "float"]
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},
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"min_samples_leaf":{
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"name":"叶子节点最小样本数",
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"value":1,
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"type":["int", "float"]
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}
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},
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"tuning parameters":{
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"description":"full",
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"criterion":{
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"name":"节点分裂放方式",
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"value":["gini", "entropy"],
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"type":["string"]
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},
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"splitter":{
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"name":"分裂节点选择策略",
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"value":["best", "random"],
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"type":["string"]
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},
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"max_depth":{
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"name":"最大深度",
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"value":["None", 2, 3, 50, 100, 500],
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"type":["int", "None"]
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},
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"max_features":{
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"name":"节点分裂最大特征数",
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"value":["None"],
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"type":["double"]
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},
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"min_samples_split":{
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"name":"节点分裂最小样本数",
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"value":[2, 5, 10],
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"type":["int", "float"]
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},
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"min_samples_leaf":{
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"name":"叶子节点最小样本数",
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"value":[1, 5, 10],
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"type":["int", "float"]
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}
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}
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}
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},
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"RandomForestClassifier":{
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"name":"随机森林分类",
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"parameters":{
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"default parameters":{
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"description":"full",
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"n_estimators ":{
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"name":"决策树数目",
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"value":100,
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"type":["int"]
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},
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"max_depth":{
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"name":"最大深度",
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"value":"None",
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"type":["int", "None"]
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},
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"criterion":{
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"name":"叶节点分裂规则",
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"value":"gini",
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"type":["string"]
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},
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"max_features":{
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"name":"节点分裂最大特征数",
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"value":"auto",
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"type":["string", "int"]
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},
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"min_samples_split":{
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"name":"节点分裂最小样本数",
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"value":2,
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"type":["int"]
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},
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"min_samples_leaf":{
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"name":"叶子节点最小样本数",
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"value":1,
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"type":["int"]
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},
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"bootstrap":{
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"name":"自助重采样",
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"value":"True",
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"type":["bool"]
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},
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"oob_score":{
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"name":"计算袋外得分",
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"value":"False",
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"type":["bool"]
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}
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},
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"tuning parameters":{
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"description":"full",
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"n_estimators":{
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"name":"决策树数目",
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"value":[5, 50, 100, 500],
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"type":["int"]
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},
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"max_depth":{
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"name":"最大深度",
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"value":["None", 2, 3, 50, 100, 500],
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"type":["int", "None"]
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},
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"criterion":{
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"name":"叶节点分裂规则",
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"value":["gini", "entropy"],
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"type":["string"]
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},
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"max_features":{
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"name":"节点分裂最大特征数",
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"value":["None"],
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"type":["double"]
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},
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"min_samples_split":{
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"name":"节点分裂最小样本数",
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"value":[2, 5, 10],
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"type":["int", "float"]
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},
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"min_samples_leaf":{
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"name":"叶子节点最小样本数",
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"value":[1, 5, 10],
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"type":["int", "float"]
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}
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}
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}
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},
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"GradientBoostingClassifier":{
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"name":"梯度提升树分类",
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"parameters":{
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"default parameters":{
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"description":"full",
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"n_estimators ":{
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"name":"提升树数目",
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"value":100,
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"type":["int"]
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},
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"loss":{
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"name":"损失函数",
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"value":"deviance",
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"type":["string"]
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},
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"learning_rate":{
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"name":"学习率",
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"value":0.1,
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"type":["float"]
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},
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"max_depth":{
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"name":"最大深度",
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"value":3,
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"type":["int"]
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},
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"criterion":{
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"name":"叶节点分裂规则",
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"value":"friedman_mse",
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"type":["string"]
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},
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"max_features":{
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"name":"节点分裂最大特征数",
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"value":"None",
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"type":["double"]
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},
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"min_samples_split":{
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"name":"节点分裂最小样本数",
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"value":2,
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"type":["int", "float"]
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},
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"min_samples_leaf":{
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"name":"叶子节点最小样本数",
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"value":1,
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"type":["int"]
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},
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"tol":{
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"name":"误差下限",
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"value":1e-4,
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"type":["float"]
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}
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},
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"tuning parameters":{
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"description":"full",
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"n_estimators":{
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"name":"提升树数目",
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"value":[5, 50, 100, 500],
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"type":["int"]
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},
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"loss":{
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"name":"损失函数",
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"value":["deviance"],
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"type":["string"]
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},
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"learning_rate":{
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"name":"学习率",
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"value":[0.1, 0.005, 0.0001],
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"type":["float"]
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},
|
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"max_depth":{
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"name":"最大深度",
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"value":["None", 2, 3, 50, 100],
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"type":["int"]
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},
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"criterion":{
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"name":"叶节点分裂规则",
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"value":["mse", "friedman_mse"],
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"type":["string"]
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},
|
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"max_features":{
|
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"name":"节点分裂最大特征数",
|
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"value":["None"],
|
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"type":["double"]
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},
|
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"min_samples_split":{
|
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"name":"节点分裂最小样本数",
|
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"value":[2, 5, 10],
|
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"type":["int", "float"]
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},
|
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"min_samples_leaf":{
|
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"name":"叶子节点最小样本数",
|
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"value":[1, 5, 10],
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"type":["int", "float"]
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},
|
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"tol":{
|
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"name":"误差下限",
|
|
"value":[1e-4, 1e-3],
|
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"type":["float"]
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}
|
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}
|
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}
|
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},
|
|
|
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"KMeans":{
|
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"name":"K-Means聚类",
|
|
"parameters":{
|
|
"default parameters":{
|
|
"description":"full",
|
|
"n_clusters":{
|
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"name":"聚类数",
|
|
"value":8,
|
|
"type":["int"]
|
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},
|
|
"init":{
|
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"name":"参数初始化算法",
|
|
"value":"k-means++",
|
|
"type":["string"]
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|
},
|
|
"n_init":{
|
|
"name":"质心初始化次数",
|
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"value":10,
|
|
"type":["float"]
|
|
},
|
|
"max_iter":{
|
|
"name":"迭代次数",
|
|
"value":300,
|
|
"type":["int"]
|
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},
|
|
"algorithm":{
|
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"name":"k-Means算法",
|
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"value":"auto",
|
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"type":["double"]
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},
|
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"tol":{
|
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"name":"误差下限",
|
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"value":1e-4,
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"type":["float"]
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}
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},
|
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"tuning parameters":{
|
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"description":"full",
|
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"init":{
|
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"name":"参数初始化算法",
|
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"value":["k-means++"],
|
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"type":["string"]
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},
|
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"n_init":{
|
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"name":"质心初始化次数",
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"value":[3, 10, 20, 50],
|
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"type":["float"]
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},
|
|
"max_iter":{
|
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"name":"迭代次数",
|
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"value":[100, 300, 500, 1000],
|
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"type":["int"]
|
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},
|
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"algorithm":{
|
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"name":"k-Means算法",
|
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"value":["auto", "full", "elkan"],
|
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"type":["double"]
|
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},
|
|
"tol":{
|
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"name":"误差下限",
|
|
"value":[1e-4, 1e-3, 1e-2],
|
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"type":["float"]
|
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}
|
|
}
|
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}
|
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},
|
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|
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"LinearRegression":{
|
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"name":"线性回归",
|
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"parameters":{
|
|
"default parameters":{
|
|
"description":"null"
|
|
},
|
|
"tuning parameters":{
|
|
"description":"null"
|
|
}
|
|
}
|
|
},
|
|
|
|
"Ridge":{
|
|
"name":"岭回归",
|
|
"parameters":{
|
|
"default parameters":{
|
|
"description":"full",
|
|
"alpha":{
|
|
"name":"正则化强度",
|
|
"value":1.0,
|
|
"type":["float", "list"]
|
|
},
|
|
"max_iter":{
|
|
"name":"最大迭代次数",
|
|
"value":"None",
|
|
"type":["None", "int"]
|
|
}
|
|
},
|
|
"tuning parameters":{
|
|
"description":"full",
|
|
"alpha":{
|
|
"name":"正则化强度",
|
|
"value":[1.0, 2.0, 5.0],
|
|
"type":["float", "list"]
|
|
},
|
|
"max_iter":{
|
|
"name":"最大迭代次数",
|
|
"value":[10, 50, 100, 500, 1000],
|
|
"type":["None","int"]
|
|
}
|
|
}
|
|
}
|
|
},
|
|
|
|
"Lasso":{
|
|
"name":"套索回归",
|
|
"parameters":{
|
|
"default parameters":{
|
|
"description":"full",
|
|
"alpha":{
|
|
"name":"正则化强度",
|
|
"value":1.0,
|
|
"type":["float", "list"]
|
|
},
|
|
"max_iter":{
|
|
"name":"最大迭代次数",
|
|
"value":"None",
|
|
"type":["None", "int"]
|
|
},
|
|
"tol":{
|
|
"name":"优化下限",
|
|
"value":1e-4,
|
|
"type":["float"]
|
|
}
|
|
},
|
|
"tuning parameters":{
|
|
"description":"full",
|
|
"alpha":{
|
|
"name":"正则化强度",
|
|
"value":[1.0, 2.0, 5.0, 10.0],
|
|
"type":["float", "list"]
|
|
},
|
|
"max_iter":{
|
|
"name":"最大迭代次数",
|
|
"value":[10, 50, 100, 200, 500, 1000],
|
|
"type":["None", "int"]
|
|
},
|
|
"tol":{
|
|
"name":"优化下限",
|
|
"value":[1e-4, 5e-3, 1e-3, 5e-2, 1e-2],
|
|
"type":["float"]
|
|
}
|
|
}
|
|
}
|
|
},
|
|
|
|
"SVR":{
|
|
"name":"支持向量回归",
|
|
"parameters":{
|
|
"default parameters":{
|
|
"description":"full",
|
|
"kernel":{
|
|
"name":"核函数",
|
|
"value":"rbf",
|
|
"type":["string"]
|
|
},
|
|
"degree":{
|
|
"name":"核函数阶数",
|
|
"value":3,
|
|
"type":["int"]
|
|
},
|
|
"gamma":{
|
|
"name":"核函数系数",
|
|
"value":"auto",
|
|
"type":["string"]
|
|
},
|
|
"coef0":{
|
|
"name":"核函数常数项",
|
|
"value":0.0,
|
|
"type":["float"]
|
|
},
|
|
"tol":{
|
|
"name":"容忍停止标准",
|
|
"value":1e-3,
|
|
"type":["float"]
|
|
},
|
|
"C":{
|
|
"name":"误差项惩罚系数",
|
|
"value":1.0,
|
|
"type":["int"]
|
|
},
|
|
"epsilon":{
|
|
"name":"epsilon",
|
|
"value":0.1,
|
|
"type":["float"]
|
|
},
|
|
"max_iter":{
|
|
"name":"迭代次数",
|
|
"value":-1,
|
|
"type":["int"]
|
|
}
|
|
},
|
|
"tuning parameters":{
|
|
"description":"full",
|
|
"kernel":{
|
|
"name":"核函数",
|
|
"value":["linear", "poly", "rbf", "sigmoid"],
|
|
"type":["string"]
|
|
},
|
|
"degree":{
|
|
"name":"核函数阶数",
|
|
"value":[3, 7, 10],
|
|
"type":["int"]
|
|
},
|
|
"gamma":{
|
|
"name":"核函数系数",
|
|
"value":["scale", "auto"],
|
|
"type":["string"]
|
|
},
|
|
"coef0":{
|
|
"name":"核函数常数项",
|
|
"value":[0.0, 0.05, 0.1, 0.9],
|
|
"type":["float"]
|
|
},
|
|
"tol":{
|
|
"name":"容忍停止标准",
|
|
"value":[1e-3, 1e-2],
|
|
"type":["float"]
|
|
},
|
|
"C":{
|
|
"name":"误差项惩罚系数",
|
|
"value":[1.0, 2.0, 10.0],
|
|
"type":["int"]
|
|
},
|
|
"epsilon":{
|
|
"name":"epsilon",
|
|
"value":[0.1, 0.2, 0.8],
|
|
"type":["float"]
|
|
},
|
|
"max_iter":{
|
|
"name":"迭代次数",
|
|
"value":[-1],
|
|
"type":["int"]
|
|
}
|
|
}
|
|
}
|
|
},
|
|
|
|
"MLPRegressor":{
|
|
"name":"多层感知器回归",
|
|
"parameters":{
|
|
"default parameters":{
|
|
"description":"full",
|
|
"hidden_layer_sizes":{
|
|
"name":"网络结构",
|
|
"value":100,
|
|
"type":["tuple"]
|
|
},
|
|
"activation":{
|
|
"name":"激活函数",
|
|
"value":"relu",
|
|
"type":["string"]
|
|
},
|
|
"solver":{
|
|
"name":"优化算法",
|
|
"value":"adam",
|
|
"type":["string"]
|
|
},
|
|
"alpha":{
|
|
"name":"L2惩罚项系数",
|
|
"value":1e-4,
|
|
"type":["float"]
|
|
},
|
|
"batch_size":{
|
|
"name":"批次大小",
|
|
"value":"auto",
|
|
"type":["int", "string"]
|
|
},
|
|
"max_iter":{
|
|
"name":"最大迭代次数",
|
|
"value":200,
|
|
"type":["double"]
|
|
},
|
|
"learning_rate_init":{
|
|
"name":"初始学习率",
|
|
"value":0.001,
|
|
"type":["double"]
|
|
}
|
|
},
|
|
"tuning parameters":{
|
|
"description":"full",
|
|
"hidden_layer_sizes":{
|
|
"name":"网络结构",
|
|
"value":[[3],[5],[5,5],[10, 10],[15,15],[100,100]],
|
|
"type":["tuple"]
|
|
},
|
|
"activation":{
|
|
"name":"激活函数",
|
|
"value":["logistic", "tanh", "relu"],
|
|
"type":["string"]
|
|
},
|
|
"solver":{
|
|
"name":"优化算法",
|
|
"value":["lbfgs", "sgd"],
|
|
"type":["string"]
|
|
},
|
|
"alpha":{
|
|
"name":"L2惩罚项系数",
|
|
"value":[1e-3, 3e-3, 5e-3, 7e-3, 8e-3],
|
|
"type":["float"]
|
|
},
|
|
"batch_size":{
|
|
"name":"批次大小",
|
|
"value":["auto"],
|
|
"type":["int", "string"]
|
|
},
|
|
"max_iter":{
|
|
"name":"最大迭代次数",
|
|
"value":[-1],
|
|
"type":["double"]
|
|
},
|
|
"learning_rate_init":{
|
|
"name":"初始学习率",
|
|
"value":[0.01, 0.001, 0.0001],
|
|
"type":["double"]
|
|
}
|
|
}
|
|
}
|
|
},
|
|
|
|
"DecisionTreeRegressor":{
|
|
"name":"决策树回归",
|
|
"parameters":{
|
|
"default parameters":{
|
|
"description":"full",
|
|
"criterion":{
|
|
"name":"节点分裂放方式",
|
|
"value":"mse",
|
|
"type":["string"]
|
|
},
|
|
"splitter":{
|
|
"name":"分裂节点选择策略",
|
|
"value":"best",
|
|
"type":["string"]
|
|
},
|
|
"max_depth":{
|
|
"name":"最大深度",
|
|
"value":"None",
|
|
"type":["int", "None"]
|
|
},
|
|
"max_features":{
|
|
"name":"节点分裂最大特征数",
|
|
"value":"None",
|
|
"type":["double"]
|
|
},
|
|
"min_samples_split":{
|
|
"name":"节点分裂最小样本数",
|
|
"value":2,
|
|
"type":["int", "float"]
|
|
},
|
|
"min_samples_leaf":{
|
|
"name":"叶子节点最小样本数",
|
|
"value":1,
|
|
"type":["int", "float"]
|
|
}
|
|
},
|
|
"tuning parameters":{
|
|
"description":"full",
|
|
"criterion":{
|
|
"name":"节点分裂放方式",
|
|
"value":["mse", "friedman_mse", "mae"],
|
|
"type":["string"]
|
|
},
|
|
"splitter":{
|
|
"name":"分裂节点选择策略",
|
|
"value":["best", "random"],
|
|
"type":["string"]
|
|
},
|
|
"max_depth":{
|
|
"name":"最大深度",
|
|
"value":["None", 2, 3, 50, 100, 500],
|
|
"type":["int", "None"]
|
|
},
|
|
"max_features":{
|
|
"name":"节点分裂最大特征数",
|
|
"value":["None"],
|
|
"type":["double"]
|
|
},
|
|
"min_samples_split":{
|
|
"name":"节点分裂最小样本数",
|
|
"value":[2, 5, 10],
|
|
"type":["int", "float"]
|
|
},
|
|
"min_samples_leaf":{
|
|
"name":"叶子节点最小样本数",
|
|
"value":[1, 5, 10],
|
|
"type":["int", "float"]
|
|
}
|
|
}
|
|
}
|
|
},
|
|
|
|
"RandomForestRegressor":{
|
|
"name":"随机森林回归",
|
|
"parameters":{
|
|
"default parameters":{
|
|
"description":"full",
|
|
"n_estimators ":{
|
|
"name":"决策树数目",
|
|
"value":100,
|
|
"type":["int"]
|
|
},
|
|
"max_depth":{
|
|
"name":"最大深度",
|
|
"value":"None",
|
|
"type":["int", "None"]
|
|
},
|
|
"criterion":{
|
|
"name":"叶节点分裂规则",
|
|
"value":"mse",
|
|
"type":["string"]
|
|
},
|
|
"max_features":{
|
|
"name":"节点分裂最大特征数",
|
|
"value":"auto",
|
|
"type":["string", "int"]
|
|
},
|
|
"min_samples_split":{
|
|
"name":"节点分裂最小样本数",
|
|
"value":2,
|
|
"type":["int"]
|
|
},
|
|
"min_samples_leaf":{
|
|
"name":"叶子节点最小样本数",
|
|
"value":1,
|
|
"type":["int"]
|
|
},
|
|
"bootstrap":{
|
|
"name":"自助重采样",
|
|
"value":"True",
|
|
"type":["bool"]
|
|
},
|
|
"oob_score":{
|
|
"name":"计算袋外得分",
|
|
"value":"False",
|
|
"type":["bool"]
|
|
}
|
|
},
|
|
"tuning parameters":{
|
|
"description":"full",
|
|
"n_estimators":{
|
|
"name":"决策树数目",
|
|
"value":[50, 100, 500, 1000, 2000],
|
|
"type":["int"]
|
|
},
|
|
"max_depth":{
|
|
"name":"最大深度",
|
|
"value":["None"],
|
|
"type":["int", "None"]
|
|
},
|
|
"criterion":{
|
|
"name":"叶节点分裂规则",
|
|
"value":["mse"],
|
|
"type":["string"]
|
|
},
|
|
"max_features":{
|
|
"name":"节点分裂最大特征数",
|
|
"value":["None"],
|
|
"type":["double"]
|
|
},
|
|
"min_samples_split":{
|
|
"name":"节点分裂最小样本数",
|
|
"value":[2],
|
|
"type":["int", "float"]
|
|
},
|
|
"min_samples_leaf":{
|
|
"name":"叶子节点最小样本数",
|
|
"value":[1],
|
|
"type":["int", "float"]
|
|
}
|
|
}
|
|
}
|
|
},
|
|
|
|
"GradientBoostingRegressor":{
|
|
"name":"梯度提升树回归",
|
|
"parameters":{
|
|
"default parameters":{
|
|
"description":"full",
|
|
"n_estimators ":{
|
|
"name":"提升树数目",
|
|
"value":100,
|
|
"type":["int"]
|
|
},
|
|
"loss":{
|
|
"name":"损失函数",
|
|
"value":"ls",
|
|
"type":["string"]
|
|
},
|
|
"learning_rate":{
|
|
"name":"学习率",
|
|
"value":0.1,
|
|
"type":["float"]
|
|
},
|
|
"max_depth":{
|
|
"name":"最大深度",
|
|
"value":3,
|
|
"type":["int"]
|
|
},
|
|
"criterion":{
|
|
"name":"叶节点分裂规则",
|
|
"value":"friedman_mse",
|
|
"type":["string"]
|
|
},
|
|
"max_features":{
|
|
"name":"节点分裂最大特征数",
|
|
"value":"None",
|
|
"type":["double"]
|
|
},
|
|
"min_samples_split":{
|
|
"name":"节点分裂最小样本数",
|
|
"value":2,
|
|
"type":["int", "float"]
|
|
},
|
|
"min_samples_leaf":{
|
|
"name":"叶子节点最小样本数",
|
|
"value":1,
|
|
"type":["int"]
|
|
},
|
|
"tol":{
|
|
"name":"误差下限",
|
|
"value":1e-4,
|
|
"type":["float"]
|
|
}
|
|
},
|
|
"tuning parameters":{
|
|
"description":"full",
|
|
"n_estimators":{
|
|
"name":"提升树数目",
|
|
"value":[5, 50, 100, 500],
|
|
"type":["int"]
|
|
},
|
|
"loss":{
|
|
"name":"损失函数",
|
|
"value":["ls", "lad"],
|
|
"type":["string"]
|
|
},
|
|
"learning_rate":{
|
|
"name":"学习率",
|
|
"value":[0.1, 0.005, 0.0001],
|
|
"type":["float"]
|
|
},
|
|
"max_depth":{
|
|
"name":"最大深度",
|
|
"value":["None", 2, 3, 50, 100],
|
|
"type":["int"]
|
|
},
|
|
"criterion":{
|
|
"name":"叶节点分裂规则",
|
|
"value":["mse", "friedman_mse"],
|
|
"type":["string"]
|
|
},
|
|
"max_features":{
|
|
"name":"节点分裂最大特征数",
|
|
"value":["None"],
|
|
"type":["double"]
|
|
},
|
|
"min_samples_split":{
|
|
"name":"节点分裂最小样本数",
|
|
"value":[2, 5, 10],
|
|
"type":["int", "float"]
|
|
},
|
|
"min_samples_leaf":{
|
|
"name":"叶子节点最小样本数",
|
|
"value":[1, 5, 10],
|
|
"type":["int", "float"]
|
|
},
|
|
"tol":{
|
|
"name":"误差下限",
|
|
"value":[1e-4, 1e-3],
|
|
"type":["float"]
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} |