在Numl.net中用于训练数据的后备存储，并添加到其中以提高准确性

我对Numl.net库非常感兴趣，它可以扫描传入的电子邮件并提取数据位。举个例子，假设我想从一封电子邮件中提取一个客户参考号码，它可以在主题行或正文内容中。

void Main()
{
    // get the descriptor that describes the features and label from the training objects
    var descriptor = Descriptor.Create<Email>();
    // create a decision tree generator and teach it about the Email descriptor
    var decisionTreeGenerator = new DecisionTreeGenerator(descriptor);
    // load the training data
    var repo = new EmailTrainingRepository(); // inject this
    var trainingData = repo.LoadTrainingData(); // returns List<Email>
    // create a model based on our training data using the decision tree generator
    var decisionTreeModel = decisionTreeGenerator.Generate(trainingData);
    // create an email that should find C4567890
    var example1 = new Email
    {
        Subject = "Regarding my order C4567890",
        Body = "I am very unhappy with your level of service. My order has still not arrived."
    };
    // create an email that should find C89779237
    var example2 = new Email
    {
        Subject = "I want to return my goods",
        Body = "My customer number is C89779237 and I want to return my order."
    };
    // create an email that should find C3239544-1
    var example3 = new Email
    {
        Subject = "Customer needs an electronic invoice",
        Body = "Please reissue the invoice as a PDF for customer C3239544-1."
    };
    var email1 = decisionTreeModel.Predict<Email>(example1);
    var email2 = decisionTreeModel.Predict<Email>(example2);
    var email3 = decisionTreeModel.Predict<Email>(example3);
    Console.WriteLine("The example1 was predicted as {0}", email1.CustomerNumber);

    if (ReadBool("Was this answer correct? Y/N"))
    {
        repo.Add(email1);
    }
    Console.WriteLine("The example2 was predicted as {0}", email2.CustomerNumber);
    if (ReadBool("Was this answer correct? Y/N"))
    {
        repo.Add(email2);
    }
    Console.WriteLine("The example3 was predicted as {0}", email3.CustomerNumber);
    if (ReadBool("Was this answer correct? Y/N"))
    {
        repo.Add(email3);
    }
}
// Define other methods and classes here
public class Email
{
   // Subject
   [Feature]
   public string Subject { get; set; }
   // Body
   [Feature]
   public string Body { get; set; }
   [Label]
   public string CustomerNumber { get; set; } // This is the label or value that we wish to predict based on the supplied features
}
static bool ReadBool(string question)
{
    while (true)
    {
        Console.WriteLine(question);
        String r = (Console.ReadLine() ?? "").ToLower();
        if (r == "y")
            return true;
        if (r == "n")
            return false;
        Console.WriteLine("!!Please Select a Valid Option!!");
    }
}

有一些事情我还没有完全理解。

1. 在一个受监督的网络中，我是否需要在每次运行应用程序时重新构建决策树，或者我是否可以以某种方式存储它，然后在需要时重新加载它?我试图节省处理时间，以便每次都重建决策树。

2. 此外，当数据得到人类验证时，网络能否不断添加自己的训练数据?也就是说，我们有一个初始的训练集，网络决定一个结果，如果一个人说"做得好"，新的例子就会被添加到训练集中，以改进它。当网络出错时，反之亦然。我想我可以在一个人验证了预测是正确的之后把它加到训练集中?我的repo.Add(email)看起来是一个合乎逻辑的方法吗?

3. 如果我添加到训练数据，在什么时候训练数据变得"超过需要"?