45 points by ml_newbie 1 year ago flag hide 16 comments
gnuser 1 year ago next
Nice question! I'd recommend looking into hyperparameter tuning to improve the accuracy of your model.
helpfulhner 1 year ago next
@gnuser I agree, hyperparameter tuning could definitely help. You can try using a tool like Optuna or Hyperopt for efficient hyperparameter tuning.
stats_pro 1 year ago prev next
It would be helpful to know more about the dataset and the current metrics of your model. Can you provide more information about the problem?
asker 1 year ago next
@stats_pro Sure, I'm working on a binary classification problem with an imbalanced dataset. The model is currently achieving around 80% accuracy, but I'm looking to improve it.
ml_engineer 1 year ago next
@asker Consider using techniques such as oversampling or undersampling to deal with the imbalanced dataset. You might also want to look into cost-sensitive learning.
another_user 1 year ago prev next
What kind of model are you using right now? Have you tried any other models for this task?
asker 1 year ago next
@another_user I'm currently using a logistic regression model, but I will consider trying other models.
more_advice 1 year ago next
@asker Logistic regression is actually a well-suited model for imbalanced datasets, but you can still experiment with other models like decision trees, random forests, or even neural networks. Make sure to cross-validate your results, though.
data_collector 1 year ago prev next
Are you using any feature engineering techniques to extract valuable features for your model?
asker 1 year ago next
@data_collector Yes, I tried some basic feature engineering techniques, such as one-hot-encoding and binary feature scaling.
expert_feature_eng 1 year ago next
@asker Great start! Have you ever considered using advanced feature engineering methods like polynomial features or feature groups? They can provide an extra boost for simple models like logistic regression.
model_interpreter 1 year ago prev next
Does your model accurately interpret the coefficients and their meaning? Maybe you can provide more context by investigating the interpretation of your model.
asker 1 year ago next
@model_interpreter I didn't check the coefficient interpretation yet. I'll give it a try and examine if there is a way to make the model more interpretable.
ensemble_person 1 year ago prev next
Ensemble methods can help you improve the overall performance of the model. You could try stacking, bagging, or boosting.
asker 1 year ago next
@ensemble_person I'm not familiar with some of those methods. Could you please provide more details or resources on how I can learn about them?
ensemble_person 1 year ago next
@asker Sure! I'd recommend taking a look at the 'Introduction to Statistical Learning' book for some great explanations on ensemble methods. You can also find great tutorials online about stacking, bagging (Random Forest), and boosting (XGBoost, LightGBM).