(y_train, y_test)
return new_y_train, new_y_test
Transform label to min equal zero and continuous
    For example if we have [1,3,4] --->  [0,1,2]
Transform label to min equal zero and continuous
    For example if we have [1,3,4] --->  [0,1,2]
Transform label to min equal zero and continuous For example if we have [ 1 3 4 ] --- > [ 0 1 2 ]
def transform_labels(y_train, y_test):
    """
    Transform label to min equal zero and continuous
    For example if we have [1,3,4] --->  [0,1,2]
    """
    # no validation split
    # init the encoder
    encoder = LabelEncoder()
    # concat train and test to fit
    y_train_test = np.concatenate((y_train, y_test), axis=0)
    # fit the encoder
    encoder.fit(y_train_test)
    # transform to min zero and continuous labels
    new_y_train_test = encoder.transform(y_train_test)
    # resplit the train and test
    new_y_train = new_y_train_test[0:len(y_train)]
    new_y_test = new_y_train_test[len(y_train):]
    return new_y_train, new_y_test
def transform_labels ( y_train , y_test ) : # no validation split # init the encoder encoder = LabelEncoder ( ) # concat train and test to fit y_train_test = np . concatenate ( ( y_train , y_test ) , axis = 0 ) # fit the encoder encoder . fit ( y_train_test ) # transform to min zero and continuous labels new_y_train_test = encoder . transform ( y_train_test ) # resplit the train and test new_y_train = new_y_train_test [ 0 : len ( y_train ) ] new_y_test = new_y_train_test [ len ( y_train ) : ] return new_y_train , new_y_test