Home/Machine Learning
  1. Asked: May 31, 2025In:

    I'm dealing with an imbalanced dataset. What methods have you used to address this issue?

    Rety1
    Rety1 Begginer

    Ah yes, the imbalanced dataset problem. I’ve come across this quite a few times, especially when working on classification tasks like fraud detection or medical predictions where one class significantly outnumbers the other. Over the years, I’ve learned that addressing it usually requires trying a mRead more

    Ah yes, the imbalanced dataset problem. I’ve come across this quite a few times, especially when working on classification tasks like fraud detection or medical predictions where one class significantly outnumbers the other.
    Over the years, I’ve learned that addressing it usually requires trying a mix of techniques rather than just depending on one approach.
    One method I often use is resampling. When the dataset is relatively small, I’ve had good success with SMOTE, which creates synthetic samples for the minority class. It helps balance things out without simply duplicating data.
    In some cases, especially with larger datasets, I’ve also used undersampling on the majority class to even things out without losing too much important information.
    Another thing I focus on is choosing the right evaluation metrics. Accuracy can be really misleading with imbalanced data, so I usually rely on metrics like precision, recall, F1-score, and AUC-ROC to get a better understanding of how well the model is actually performing.
    In a lot of models, I’ve also used class weights. Most libraries like scikit-learn or XGBoost allow you to give more importance to the minority class during training, which helps the model learn better distinctions.
    And when the problem is more complex, ensemble methods like balanced random forests or gradient boosting models with built-in sampling techniques have worked well for me.
    They’re not a perfect solution on their own, but combined with smart evaluation and a good understanding of the domain, they can definitely improve performance on imbalanced data.

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  2. Asked: May 31, 2025In:

    Please tell me your approach for feature selection in your machine learning projects?

    Hassaan Arif
    Hassaan Arif Enlightened

    When it comes to feature selection, my approach is a bit like dating apps I swipe left on features that don’t add value and swipe right on those that actually improve the relationship (aka model performance). First, I start with the basics: get rid of features that are basically just noise or have zRead more

    When it comes to feature selection, my approach is a bit like dating apps

    I swipe left on features that don’t add value and swipe right on those that actually improve the relationship (aka model performance).

    First, I start with the basics: get rid of features that are basically just noise or have zero variance no point dating someone who never changes, right?

    Then I check correlations if two features are basically twins, I keep one to avoid awkward love triangles in the model.

    Next, I use some automated tools like Recursive Feature Elimination or tree-based feature importance to let the data do the heavy lifting kind of like letting your friends give honest opinions.

    Finally, I test my “matches” with cross-validation to make sure they’re not just a good look on paper but actually perform well in the wild.

    In short, I treat feature selection like finding the perfect date: a bit of instinct, a dash of science, and a lot of trial and error!

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  3. Asked: May 29, 2025In:

    I trained my model, but it's performing too well on validation — could this be data leakage? How do I check for that?

    joseph1
    joseph1 Begginer

    I once trained a model that was performing way too well on the validation set — like, suspiciously good. At first, I was excited… but something felt off. Turned out, it was data leakage. Here’s what I did to figure it out: I rechecked my data splits and found that some similar entries had ended up iRead more

    I once trained a model that was performing way too well on the validation set — like, suspiciously good. At first, I was excited… but something felt off. Turned out, it was data leakage.
    Here’s what I did to figure it out:

    • I rechecked my data splits and found that some similar entries had ended up in both training and validation.
    • I reviewed my features — one of them was indirectly revealing the target.
    • I even tested a basic model, and it still performed too well, which confirmed my suspicion.

    Lesson learned: if your model feels like it’s “too perfect,” always check for leakage. It’ll save you a ton of headaches later. Adopt this, It may solve this problem.

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