Selection¶

Note: Make sure to correct for multiple hypothesis testing

Model Selection¶

1. Fit multiple models \(g_i\) on the training data and eyeball dev data
2. Use dev data for hyper parameter tuning of each model \(g_i\)
3. Use external validation data for model selection and obtain \(g^*\)
4. Combine the training and validation data. Refit \(g^*\) on this set to obtain \(g^{**}\)
5. Assess the performance of \(g^{**}\) on the test data

Finally, train \(g^{**}\) on the entire data to obtain \(\hat f\)

Check the results on the self-hosted competition

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