Inverse_transform方法(LabelEncoder)

Question

你可以在下面找到我在互联网上找到的代码来构建一个简单的神经网络。一切都很好。我对y标签进行了编码，这些是我得到的预测：

2 0 1 2 1 2 2 0 2 1 0 0 1 1 1 2 2 1 0 1 0 0 2

所以现在我需要把它转换回原来的Iris类(Iris-Virginica，Setosa，Versicolor)。我需要使用inverse_transform方法。你能帮忙吗？

    import pandas as pd
    from sklearn import preprocessing
    from sklearn.model_selection import train_test_split
    from sklearn.neural_network import MLPClassifier
    from sklearn.metrics import classification_report, confusion_matrix 
    
    
    # Location of dataset
    url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
    
    # Assign colum names to the dataset
    names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'Class']
    
    # Read dataset to pandas dataframe
    irisdata = pd.read_csv(url, names=names)  
    
    irisdata.head()
    #head_tableau=irisdata.head()
    #print(head_tableau)
    
    # Assign data from first four columns to X variable
    X = irisdata.iloc[:, 0:4]
    
    # Assign data from first fifth columns to y variable
    y = irisdata.select_dtypes(include=[object])  
    
    y.head()
    #afficher_y=y.head()
    #print(afficher_y)
    
    y.Class.unique()
    #affiche=y.Class.unique()
    #print(affiche)
    
    le = preprocessing.LabelEncoder()
    
    y = y.apply(le.fit_transform)  
    
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20)
    
    mlp = MLPClassifier(hidden_layer_sizes=(10, 10, 10), max_iter=1000)  
    mlp.fit(X_train, y_train.values.ravel())
    
    predictions = mlp.predict(X_test)
    print(predictions)

Answer 1

你走在正确的轨道上：

In [7]: le.inverse_transform(predictions[:5])
Out[7]: 
array(['Iris-virginica', 'Iris-setosa', 'Iris-setosa', 'Iris-versicolor',
       'Iris-virginica'], dtype=object)