【机器学习】OneVsRestClassifier的网格调参如何实现

最近在进行多分类问题中，用到了OneVsRestClassifier模型，但是遇到了一个比较困惑的问题，那就是如何有效的调参？

一、简单模型网格调参

param = {"criterion": ["gini", "entropy"], "max_depth": np.arange(1,20,1), 'n_estimators': np.arange(50, 501, 50)} 
rf_gs = GridSearchCV(estimator = RandomForestClassifier(), param_grid = param, cv = 5, scoring = "f1", n_jobs = -1, verbose = 10) 
rf_gs.fit(train_x, train_y) 
print(rf_gs.best_params_) 
y_hat = rf_gs.best_estimator_.predict(test_x) 
print(classification_report(test_y, y_hat))

二、使用OneVsRestClassifier的调参

此时我们需要在每个参数面前加上estimator__

param = {"estimator__max_depth": np.arange(1,20,1), 'estimator__n_estimators': np.arange(100, 501, 50)}
model = OneVsRestClassifier(RandomForestClassifier(random_state = 10))
clf = GridSearchCV(model,param_grid = param, scoring='f1', n_jobs = -1, cv = 5, verbose = 2)
clf.fit(X_train, y_train) 
print(clf.best_params_) 
y_hat = clf.best_estimator_.predict(X_test) 
print(classification_report(y_test, y_hat))