Answers.Body: <p>I don't think that tuning the probability threshold in a binary classification model can help improving the model accuracy.</p>
  <p>That said, if your best model is &quot;bad&quot; in the sense that it doesn't predict any positive classes then you should change your metric for evaluating the model. In your case it's probably useful to try an F1 metric, you can find more details on how to choose a good metric in imbalanced datasets here: <a href="https://www.kaggle.com/code/marcinrutecki/best-techniques-and-metrics-for-imbalanced-dataset#2.3-Choosing-metrics" rel="nofollow noreferrer">https://www.kaggle.com/code/marcinrutecki/best-techniques-and-metrics-for-imbalanced-dataset#2.3-Choosing-metrics</a>.</p>
  <p>When you have a good metric, you can loop over various hyperparameters to find the best combination.</p>
  
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Body: <p>How would I perform grid search in scikit-learn including over the probability threshold for binary classification? In my search, most answers suggest first fitting a model and then performing a loop over different threshold values. However, doesn't the model fitting itself assume a threshold of 0.5? My &quot;best&quot; model doesn't predict any positive classes due to class imbalance. How can I trust the corresponding hyperparameters when playing with the threshold later?</p>
  
Tags: <classification><binary-classification>
Title: How to incorporate the probability threshold for binary classification into scikit-learn GridSearchCV?
UserDisplayName: bonzo_pippinpaddle
ThreadText: # How to incorporate the probability threshold for binary classification into scikit-learn GridSearchCV?
  
  (Asked by: username_0 on 2024-02-16)
  
  How would I perform grid search in scikit-learn including over the probability threshold for binary classification? In my search, most answers suggest first fitting a model and then performing a loop over different threshold values. However, doesn't the model fitting itself assume a threshold of 0.5? My "best" model doesn't predict any positive classes due to class imbalance. How can I trust the corresponding hyperparameters when playing with the threshold later?
  
  **Tags:** classification, binary-classification
  
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  ## 1 answers
  
  ### Answer by username_1 on 2024-02-21. Score: 1
  
  I don't think that tuning the probability threshold in a binary classification model can help improving the model accuracy.
  
  That said, if your best model is "bad" in the sense that it doesn't predict any positive classes then you should change your metric for evaluating the model. In your case it's probably useful to try an F1 metric, you can find more details on how to choose a good metric in imbalanced datasets here: <https://www.kaggle.com/code/marcinrutecki/best-techniques-and-metrics-for-imbalanced-dataset#2.3-Choosing-metrics>.
  
  When you have a good metric, you can loop over various hyperparameters to find the best combination.
  
  
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