Как исправить эту ошибку или что я могу изменить или реализовать?
ValueError: Классификационные метрики не могут обрабатывать сочетание целых индикаторов с несколькими метками и непрерывных выходных данных
полный код прилагается ниже. Небольшая информация о имеющемся в нем файле CSV 15 классов 78 входных размеров и более 600 000 выборок
dataframe = pandas.read_csv("C:/Users/bam/train.csv", header=0, dtype=object)
dataset = dataframe.values
X_train = dataset[:,0:78].astype(float)
y_train = dataset[:,78]
dataframe = pandas.read_csv("C:/Users/bam/test.csv", header=0, dtype=object)
dataset = dataframe.values
X_test = dataset[:,0:78].astype(float)
y_test = dataset[:,78]
##my version for encoding
encoder = LabelEncoder()
encoder.fit(y_train)
encoded_Yone = encoder.transform(y_train)
# convert integers to dummy variables (i.e. one hot encoded)
y_train = np_utils.to_categorical(encoded_Yone)
#encode our testing set
encoder = LabelEncoder()
encoder.fit(y_test)
encoded_Ytwo = encoder.transform(y_test)
# convert integers to dummy variables (i.e. one hot encoded)
y_test = np_utils.to_categorical(encoded_Ytwo)
#Creating an object of StandardScaler trial run to try and improve accuracy from 90% baseline*
sc = StandardScaler()
#Scaling the data using the StandardScaler() object
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
neural_classifier = Sequential()
#output_dim = number of nuerons in first hidden layer
#init = initializing the weights of the neural network
#input_dim = number of neuron in the input layer = number of input features = 78
#Actiavtion = activation function that is used in each layer
#Dense is the type of layer
#The neural network needs to start with some weights and then iteratively update them to better values. The term kernel_initializer is a fancy term for which statistical distribution or function to use for initialising the weights. In case of statistical distribution, the library will generate numbers from that statistical distribution and use as starting weights.
#Input layer
neural_classifier.add(Dense(100, kernel_initializer = 'uniform', activation = 'relu', input_dim = 78))
neural_classifier.add(Dense(150, kernel_initializer = 'uniform', activation = 'relu'))
neural_classifier.add(Dense(200, kernel_initializer = 'uniform', activation = 'relu'))
neural_classifier.add(Dense(250, kernel_initializer = 'uniform', activation = 'relu'))
neural_classifier.add(Dense(300, kernel_initializer = 'uniform', activation = 'relu'))
neural_classifier.add(Dense(350, kernel_initializer = 'uniform', activation = 'relu'))
neural_classifier.add(Dense(400, kernel_initializer = 'uniform', activation = 'relu'))
neural_classifier.add(Dense(250, kernel_initializer = 'uniform', activation = 'relu'))
neural_classifier.add(Dense(300, kernel_initializer = 'uniform', activation = 'relu'))
# output layer has 15 neurons because there are 15 classes in dataset
#Since it is a multiclass classification problem hence we are using the softmax activation function
neural_classifier.add(Dense(15, kernel_initializer = 'uniform', activation = 'softmax'))
neural_classifier.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])
neural_classifier.fit(X_train, y_train, batch_size = 32, epochs = 2)
#Predicting the labels for the test data
y_pred = neural_classifier.predict(X_test)
#Calculating the accuracy score
accuracy = metrics.accuracy_score(y_test, y_pred)
#Calculating the precision score
precision = metrics.precision_score(y_test, y_pred)
#Calculating the recall score
recall = metrics.recall_score(y_test, y_pred, average='weighted')
#Calculating the f1-score
f1score = metrics.f1_score(y_test, y_pred, average='weighted')
print("Accuracy score of the model is :", accuracy)
print("precision score of the model is :", precision)
print("Recall score of the model is :", recall)
print("f1-score of the model is :", f1score)
полная ошибка