我有以下 Keras DNN 模型并导入了必要的 Keras & Yellowbrick 库：
optimizer = RMSprop(0.001)
finalDNNModel_wrap = KerasClassifier(build_fn=parkOptimalDNN(optimizer),
epochs=750,
batch_size=10, verbose=0)
finalDNNModel = Sequential()
finalDNNModel.add(Dense(32, input_dim=8, activation='relu'))
finalDNNModel.add(Dense(8, activation='relu'))
finalDNNModel.add(Dense(1, activation='sigmoid'))
#Compile the model
finalDNNModel.compile(loss='binary_crossentropy',optimizer=optimizer, metrics=['accuracy'])
# Fit & Evaluate on the independent validation data set
finalDNNModel.fit(X_pca_train,y_train,batch_size = 10,epochs = 750 , verbose = 0)
dnnPrediction = (finalDNNModel.predict(X_pca_validation) > 0.5).astype("int64")
dnnPredictProb = finalDNNModel.predict_proba(X_pca_validation)
我使用 YellowBrick 可视化包进行分类报告，如下所示：
classes = ["Not Parkinson", "Parkinson"]
pd.set_option('precision',2)
vizDNN = ClassificationReport(finalDNNModel,classes = classes,cmap="YlGnBu",is_fitted=True,
force_model=True,
title="DNN")
vizDNN.score(X_pca_validation, y_validation)
vizDNN.show()
它给出了错误：“YellowbrickTypeError：此估计器不是分类器；请尝试使用回归或聚类分数可视化器！” 有人可以帮忙吗