All Questions
6 questions
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1
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Top N accuracy for an imbalanced multiclass classification problem
I have a multi-class classification problem that is imbalanced. The task is about animal classification.
Since it's imbalanced, I am using macro-F1 metric and the current result that I have is: ...
- 262
0
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1
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1k
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Compare model accuracy when training with imbalanced and balanced data
So I was recently doing a data science project which is a multi class classification.
The project can be found https://www.kaggle.com/c/otto-group-product-classification-challenge.
The dataset is an ...
1
vote
0
answers
333
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g-mean for binary classification doesn't use sensitivity of each class?
scikit-learn's contrib package, imbalanced-learn, has a function, geometric_mean_score(), ...
- 111
31
votes
4
answers
65k
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macro average and weighted average meaning in classification_report
I use the "classification_report" from from sklearn.metrics import classification_report in order to evaluate the imbalanced binary classification
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- 1,864
3
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0
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806
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Target mean encoding worse than ordinal encoding with GBDT ( XGBoost, CatBoost )
I have a dataset of 23k rows of an unbalanced dataset 85/15 ratio, 10 variables ( 9 of which are categorical ) , i'm using CatBoost and XGBoost for a binary classification.
I applied cv (5 iteration ...
- 2,094
0
votes
1
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773
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Random forest with zero precision for unbalanced test data
Apologies if this is a basic question.
I have a very unbalanced dataset in which the records are labelled by one of two classes, class1 (negative class) and class2 (positive class):
class 1: 1.5 ...
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