What Validation sample is
Validation samples are a subset of data that is used to determine the accuracy of a model. The validation sample is used to evaluate the model’s performance on unseen data in order to determine whether the model is overfitting or underfitting the data. Validation samples are typically used in machine learning and predictive modeling to assess the accuracy of a model.
The steps for validation sample are as follows:
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Divide the data into two sets: a training set and a validation set.
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Train the model on the training set.
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Test the model on the validation set.
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Compute the accuracy of the model on the validation set.
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Adjust the model parameters as necessary to improve the accuracy.
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Repeat steps 2-5 until the model is accurate enough for the desired purpose.
Examples
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Validation sample can be used to assess the accuracy of a predictive model. For example, a sample can be used to compare the predicted values from the model to the actual values, to determine whether the model is producing accurate predictions.
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Validation sample can be used to verify that the data used to build a model is representative of the population being modeled. For example, a sample can be used to compare the characteristics of the data used to build the model to the characteristics of the actual population.
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Validation sample can be used to check the robustness of a model. For example, the sample can be used to check if the model performs equally well in different segments of the population or across different time periods.
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Validation sample can be used to check if a model is over-fitting the data. For example, the sample can be used to compare the performance of the model on the training data to its performance on the validation sample.