Questions tagged [imbalanced-learn]

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Balancing the dataset using imblearn undersampling, oversampling and combine?

I have the imbalanced dataset data['Class'].value_counts() Out[22]: 0 137757 1 4905 Name: Class, dtype: int64 ...
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1answer
33 views

The most informative curve for imbalance datasets

For the imbalanced datasets: Can we say the Precision-Recall curve is more informative, thus accurate, than ROC curve? Can we rely on F1-score to evaluate the skillfulness of the resulted model in ...
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Why Imblearn pipeline gives very different results when used scaler and under sampling method swapped

I am using the Kaggle's credit card fraud detection dataset (https://www.kaggle.com/mlg-ulb/creditcardfraud) In order to create a balanced datasets I was testing RandomUnderSampler() and NearMiss(). I ...
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1answer
55 views

Cross validation schema for imbalanced dataset

Based on a previous post, I understand the need to ensure that the validation folds during the CV process have the same imbalanced distribution as the original dataset when training a binary ...
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19 views

Multi-classification: low precision due to imbalanced classes in test data - what to do?

I built a multi-classification model with 3 result classes (XGBoost using R's caret-package): A, B and C. I undersampled my training data - so every class is equally abundant for training. The ...
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CART classification for imbalanced datasets with R

Hey guys i need your help for a university project. The main Task is to analyze the effects of over/under-smapling on a imbalanced Dataset. But before we can even start with that, our task sheet says, ...
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34 views

Balance two crossentropy losses with different number of neurons

I have a model with a few outputs, each output with shape: Shape: (batch_size, labels_1) -> softmax -> ...
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40 views

Binary classifier on imbalanced dataset yields weird PR curve

I have a dataset with ~6M points, 9 features and two classes. The minority class represents just under 2% of the data. The data is first divided into 100 batches and a different classifier is trained ...
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41 views

Different result between Rapidminer and Python imblearn

I'm currently working on imbalanced classification problem. However i found different result between SMOTE in rapidminer and SMOTE in imblearn (python). rapidminer SMOTE give 15-20% improvement on ...
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13 views

How many instances should be synthesized for each class when using over-sampling techniques?

As for an imbalanced multi-class dataset, how many instances should be synthesized for each class if we use over-sampling techniques such as SMOTE? For example, there is 4 class including 'A', 'B', 'C'...
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12 views

How does my score ranges change when I use class weight in Keras vs when I don't?

Following are images of Unweighted Score Range and Weighted Score Range
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21 views

dealing with imbalanced data for multi-class problem

Based on the experiments I run for a number of times, and the reading I did on imbalanced data for a multiclassification problem such as this paper, resampling techniques like ...
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24 views

Predict best score on unlabelled test set

Data I have one dataset with $1500$ data points, each with $\sim 23 000$ features (gene expression data, if that matters). However, I've split this dataset into a labelled training set of size 1000, ...
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1answer
70 views

Preferred approaches for imbalanced data

I am building a binary classification model with imbalanced target variable (13% Class 1 vs 87% class 0). I am considering the following three options to handle the data imbalance Option1: Create a ...
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1answer
304 views

Positively skewed target label in regression

I have a dataset where the target label is positively skewed and produces a long tail, and currently I have a high residual on these values when experimenting with some linear, tree-based and neural-...
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94 views

Deep learning(MLP) on multiclass classification. Model learns only one class

I am new to deep learning. I have imbalanced class data. I used one hot encoding and scaling to preprocess my data. I have used adamoptimizer as optimizer function and sparse categorical crossentropy ...