All Questions
Tagged with class-imbalance accuracy
13 questions
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332
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Is there a way to focus mainly on high precision when fitting a tree model?
I have a dataset with 95% false and 5% true labels, some 200000 samples overall, I'm fitting a LightGBM model. I mainly need to focus on high precision and have low number of false positives, I don't ...
- 1
0
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1
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147
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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 ...
0
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1
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49
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Machine learning accuracy for not a class-imbalanced problem
I would like know if the accuracy has an impact on not class-imbalanced dataset ? I know that accuracy is sensitive to class-imbalance and also always good to be able to appreciate precision and ...
- 147
0
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142
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What do you suggest to increase the sensitivity rates in conventional ML model?
I have an imbalanced data problem (prop. rate: 0.8571429 0.1428571) and for this reason, our sensitivity and PPV rates are very low. What do you recommend to fix this problem in R or in general? See ...
- 101
1
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1
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943
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Test set larger than train set [closed]
There is a two class dataset with 1121 values in total, having 230 from same class and 891 from the other class. The training set is choosen as 230+230=460 from both classes and the test set as the ...
- 37
0
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1
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209
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Model accuracy: how to determine it?
I have some doubts regarding the approach to building a classifier such as Multinomial Naive Bayes or SVM. I will go through the steps to see if the approach is fine. I do have not a lot of experience ...
- 295
0
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1
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87
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XG Boost result interpretation for unbalanced datasets (Accuracy & AUCROC)
My dataset is of shape – 5621*8 (binary classification)
Label/target : Success (4324, 77 %) & Not success (1297, 23 %)
(success and Not success were been ...
- 165
1
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0
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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
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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 ...
- 113
5
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3
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14k
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Is Gini coefficient a good metric for measuring predictive model performance on highly imbalanced data
I am evaluating a Credit Risk model that predicts the estimated likelihood of customers defaulting on their mortgage accounts. The model is a Logistic Regression estimator and was built by another ...
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