Yellowbrick旨在与scikit-learn一起使用，并使用sklearn的类型检查系统来检测模型是否适合特定类别的机器学习问题。如果neupy PNN模型实现了scikit-learn估计器API（例如fit()和predict()），则可以直接使用该模型并通过使用{{ 1}}参数如下：

force_model=True

但是，快速浏览neupy documentation后，由于neupy方法被命名为visualizer = ClassificationReport(model,support=True,force_model=True) 而不是train，并且由于PNN模型确实被命名，因此这似乎不一定有效不实现fit方法，也不支持后缀为score()的学习参数。

解决方案是创建一个围绕_模型的轻量级包装，将其作为sklearn估计器公开。在Yellowbrick数据集上进行测试，看来可行：

PNN

尽管Yellowbrick目前不支持neupy，但是如果您有兴趣-可能值得submitting an issue建议将neupy添加到contrib中，类似于在Yellowbrick中实现from sklearn import metrics from neupy import algorithms from sklearn.base import BaseEstimator from yellowbrick.datasets import load_occupancy from yellowbrick.classifier import ClassificationReport from sklearn.model_selection import train_test_split class PNNWrapper(algorithms.PNN,BaseEstimator): """ The PNN wrapper implements BaseEstimator and allows the classification report to score the model and understand the learned classes. """ @property def classes_(self): return self.classes def score(self,X_test,y_test): y_hat = self.predict(X_test) return metrics.accuracy_score(y_test,y_hat) # Load the binary classification dataset X,y = load_occupancy() X_train,y_train,y_test = train_test_split(X,y,test_size=0.2) # Create and train the PNN model using the sklearn wrapper model = PNNWrapper(std=0.1,verbose=True,batch_size=500) model.train(X_train,y_train) # Create the classification report viz = ClassificationReport( model,classes=["not occupied","occupied"],is_fitted=True,force_model=True,title="PNN" ) # Score the report and show it viz.score(X_test,y_test) viz.show() 的方式。