Improve model performance on unseen data












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$begingroup$


NOTE: This question was first posted on a different SO forum but I received suggestions to move it here





This is a follow-up question to a question I had previously posted on this forum
We conducted an experiment on 100 subjects and obtained a dataset that was used to train a machine learning model that works almost perfectly (we get a 99.99% accuracy) using cross-validation but fails miserably when test to new unseen data (we get only 39.8% accuracy). In detail, here is how we are testing our model:




  1. Conduct an experiment (100 subjects) and recording physiological data (skin conductance and heart rate)

  2. For each subject, compute the feature from the raw data (thus, we obtain 100 datasets with 25 features)

  3. Reserve the whole data of one subject for model validation

  4. Combine the remaining 99 datasets into one large dataset (thus, we obtain a dataset with up to 1 million rows and 25 columns)

  5. The obtained large dataset is used to train a model and evaluate it using a 10-folds cross-validation.

  6. Use the reserved dataset (see step 3) to validate the model obtained in step 5 above
    The model performs very well (99.9% accuracy) when we used cross-validation (step 5) but fails to generalize (only 40% accuracy) when tested on new data from the unseen subject (step 3).


After reading the comments I received in my previous question, I decided to revise how I validate the model. Thus, instead of removing all the sample of one subject (as I did in step 3), I only remove all but a few samples (just 0.1% of the sample, i.e around 100 samples in my case). Surprising the model performs significantly better and achieves a 90% accuracy. This is puzzling and I would like to ask a few questions:



(1)How can one explain that a few samples (just 100 samples) are responsible for such an increase in performance (from 40% to 90% accuracy)(I will need to publish my result in a reputable conference)?



(2) Is there any published work that discusses this type of phenomena?









share









$endgroup$

















    0












    $begingroup$


    NOTE: This question was first posted on a different SO forum but I received suggestions to move it here





    This is a follow-up question to a question I had previously posted on this forum
    We conducted an experiment on 100 subjects and obtained a dataset that was used to train a machine learning model that works almost perfectly (we get a 99.99% accuracy) using cross-validation but fails miserably when test to new unseen data (we get only 39.8% accuracy). In detail, here is how we are testing our model:




    1. Conduct an experiment (100 subjects) and recording physiological data (skin conductance and heart rate)

    2. For each subject, compute the feature from the raw data (thus, we obtain 100 datasets with 25 features)

    3. Reserve the whole data of one subject for model validation

    4. Combine the remaining 99 datasets into one large dataset (thus, we obtain a dataset with up to 1 million rows and 25 columns)

    5. The obtained large dataset is used to train a model and evaluate it using a 10-folds cross-validation.

    6. Use the reserved dataset (see step 3) to validate the model obtained in step 5 above
      The model performs very well (99.9% accuracy) when we used cross-validation (step 5) but fails to generalize (only 40% accuracy) when tested on new data from the unseen subject (step 3).


    After reading the comments I received in my previous question, I decided to revise how I validate the model. Thus, instead of removing all the sample of one subject (as I did in step 3), I only remove all but a few samples (just 0.1% of the sample, i.e around 100 samples in my case). Surprising the model performs significantly better and achieves a 90% accuracy. This is puzzling and I would like to ask a few questions:



    (1)How can one explain that a few samples (just 100 samples) are responsible for such an increase in performance (from 40% to 90% accuracy)(I will need to publish my result in a reputable conference)?



    (2) Is there any published work that discusses this type of phenomena?









    share









    $endgroup$















      0












      0








      0





      $begingroup$


      NOTE: This question was first posted on a different SO forum but I received suggestions to move it here





      This is a follow-up question to a question I had previously posted on this forum
      We conducted an experiment on 100 subjects and obtained a dataset that was used to train a machine learning model that works almost perfectly (we get a 99.99% accuracy) using cross-validation but fails miserably when test to new unseen data (we get only 39.8% accuracy). In detail, here is how we are testing our model:




      1. Conduct an experiment (100 subjects) and recording physiological data (skin conductance and heart rate)

      2. For each subject, compute the feature from the raw data (thus, we obtain 100 datasets with 25 features)

      3. Reserve the whole data of one subject for model validation

      4. Combine the remaining 99 datasets into one large dataset (thus, we obtain a dataset with up to 1 million rows and 25 columns)

      5. The obtained large dataset is used to train a model and evaluate it using a 10-folds cross-validation.

      6. Use the reserved dataset (see step 3) to validate the model obtained in step 5 above
        The model performs very well (99.9% accuracy) when we used cross-validation (step 5) but fails to generalize (only 40% accuracy) when tested on new data from the unseen subject (step 3).


      After reading the comments I received in my previous question, I decided to revise how I validate the model. Thus, instead of removing all the sample of one subject (as I did in step 3), I only remove all but a few samples (just 0.1% of the sample, i.e around 100 samples in my case). Surprising the model performs significantly better and achieves a 90% accuracy. This is puzzling and I would like to ask a few questions:



      (1)How can one explain that a few samples (just 100 samples) are responsible for such an increase in performance (from 40% to 90% accuracy)(I will need to publish my result in a reputable conference)?



      (2) Is there any published work that discusses this type of phenomena?









      share









      $endgroup$




      NOTE: This question was first posted on a different SO forum but I received suggestions to move it here





      This is a follow-up question to a question I had previously posted on this forum
      We conducted an experiment on 100 subjects and obtained a dataset that was used to train a machine learning model that works almost perfectly (we get a 99.99% accuracy) using cross-validation but fails miserably when test to new unseen data (we get only 39.8% accuracy). In detail, here is how we are testing our model:




      1. Conduct an experiment (100 subjects) and recording physiological data (skin conductance and heart rate)

      2. For each subject, compute the feature from the raw data (thus, we obtain 100 datasets with 25 features)

      3. Reserve the whole data of one subject for model validation

      4. Combine the remaining 99 datasets into one large dataset (thus, we obtain a dataset with up to 1 million rows and 25 columns)

      5. The obtained large dataset is used to train a model and evaluate it using a 10-folds cross-validation.

      6. Use the reserved dataset (see step 3) to validate the model obtained in step 5 above
        The model performs very well (99.9% accuracy) when we used cross-validation (step 5) but fails to generalize (only 40% accuracy) when tested on new data from the unseen subject (step 3).


      After reading the comments I received in my previous question, I decided to revise how I validate the model. Thus, instead of removing all the sample of one subject (as I did in step 3), I only remove all but a few samples (just 0.1% of the sample, i.e around 100 samples in my case). Surprising the model performs significantly better and achieves a 90% accuracy. This is puzzling and I would like to ask a few questions:



      (1)How can one explain that a few samples (just 100 samples) are responsible for such an increase in performance (from 40% to 90% accuracy)(I will need to publish my result in a reputable conference)?



      (2) Is there any published work that discusses this type of phenomena?







      machine-learning cross-validation overfitting





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      asked 4 mins ago









      LapatrieLapatrie

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