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Thread: How well

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    #1

    How well

    "in order to estimate how well your model has been trained"

    What is the meaning of "How well"?
    Last edited by emsr2d2; 21-Oct-2019 at 13:16. Reason: Removed quote box to make quoting it easier

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    #2

    Re: How well

    Please provide the entire sentence, not just that fragment.
    Remember - if you don't use correct capitalisation, punctuation and spacing, anything you write will be incorrect.

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    #3

    Re: How well

    1. Validation/Test phase: in order to estimate how well your model has been trained (that is dependent upon the size of your data, the value you would like to predict, input etc) and to estimate model properties (mean error for numeric predictors, classification errors for classifiers, recall and precision for IR-models etc.)


    https://stats.stackexchange.com/ques...validation-set
    Last edited by emsr2d2; 21-Oct-2019 at 14:06. Reason: Removed quote box (again)

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    #4

    Re: How well

    Not a teacher.

    "how well" states the condition of wellness of an action. In your example, it's an assessment of the training of the model.
    To live only for some future goal is shallow. It's the sides of the mountain that sustain life, not the top.

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    #5

    Re: How well

    Quote Originally Posted by Armin View Post
    1. Validation/Test phase: in order to estimate how well your model has been trained (that is dependent upon the size of your data, the value you would like to predict, input etc) and to estimate model properties (mean error for numeric predictors, classification errors for classifiers, recall and precision for IR-models etc.)


    https://stats.stackexchange.com/ques...validation-set
    You omitted the beginning of the sentence, though it isn't really necessary to understand the phrase you're asking about. Here's what you should have posted.

    While performing machine learning you do the following:
    1. Validation/Test phase: in order to estimate how well your model has been trained (that is dependent upon the size of your data, the value you would like to predict, input etc) and to estimate model properties (mean error for numeric predictors, classification errors for classifiers, recall and precision for IR-models etc.) .


    I ended with an ellipsis to indicate that I omitted the rest of the sentence, which isn't relevant to the question.

    Here "how well" means to what degree of perfection.
    I am not a teacher.

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