All Questions
Tagged with scikit-learnclass-imbalance
55 questions
2votes
1answer
69views
Taking into account instance cost in learning?
I am generally trying to take into account costs in learning. The set-up is as follows: a statistical learning problem with usuall X and y, where y is imbalanced (roughly 1% of ones). Scikit learn ...
- 2,614
0votes
1answer
136views
Imbalanced Cost-Sensitive Learning Workflow - How to split the data, tune hyperparameters and apply adecision threshold?
I am facing a problem with imbalanced dataset in which I would like to detect the rare event. My questions are more of general strategy about the whole workflow and I would like to hear your thoughts ...
-1votes
1answer
61views
How to deal with a heavily imbalanced test dataset?
Both my train data and test data were imbalanced. So I tried SMOTE for training. Before Smote: ...
4votes
2answers
2kviews
Flipping the labels in a binary classification gives different model and results
I have an imbalanced dataset and I want to train a binary classifier to model the dataset. Here was my approach which resulted into (relatively) acceptable performance: 1- I made a random split to get ...
- 43
1vote
0answers
1kviews
Downsampling in sklearn. Test and Train performance question
I have a class imbalanced data set, and have the following set up to handle class imbalance. I first split to test and train and only perform downsampling on the training set and then get the test ...
- 131
1vote
2answers
641views
Evaluation Metric for Imbalanced and Ordinal Classification
I'm looking for an ML evaluation metric that would work well with imbalanced and ordinal multiclass datasets: Imagine you want to predict the severity of a disease that has 4 grades of severity where ...
2votes
1answer
2kviews
Imbalanced data set with Sample weighting - How to interpret the performance metrics?
Consider a binary classification scenario whereby the True class (5%) is severely outbalanced to the False class (95%). My data set contains numeric data. I am using SKLearn and trying some different ...
0votes
1answer
1kviews
roc_auc_score from sk-learn gives error when test label vector with classes has only a subset of the whole set
I have an imbalanced dataset. Does it make sense to compute the roc-auc for the classifier I created in a holdout set? Here's very artificial MWE: ...
1vote
1answer
251views
Imbalanced classification task – Discrepancy between learning curves and test set evaluation
I have a binary classification task related to customer churn for a bank. The dataset contains 10,000 instances and 11 features. The target variable is imbalanced (80% remained as customers (0), 20% ...
- 67
2votes
1answer
367views
Training is not stable with extreme class imbalance
I'm dealing with a multi-class classification problem with around 30 categories. This problem has a severe class imbalance: Around 300 examples for the least common class. Around 100k examples for ...
- 6,116
0votes
1answer
707views
Logistic regression with unbalanced data, scoring based only on rare class
I have a dataset off app. 600.000 data points in which 0.2% (1.200 samples) is labelled as signifying a rare event. I want to use logistic regression to help me predict this rare event, but even when ...
- 15
0votes
1answer
28views
Unbalanced training set from balanced data
I am looking to get an unbalanced training set with a given ratio of classA:classB from a dataset without regarding if it is balanced or not. The point is to analyze the influence of data imbalance on ...
0votes
1answer
2kviews
How does class_weight work in Decision Tree?
I am interested in Cost-Sensitive learning. And I am trying to understand how class_weight in DecisionTree works in terms of math. I read a lot of articles that ...
- 21
0votes
2answers
974views
GridSearch on imbalanced datasets
Im trying to use gridsearch to find the best parameter for my model. Knowing that I have to implement nearmiss undersampling method while doing cross validation, should I fit my gridsearch on my ...
- 135
