Questions tagged [smote]

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50 views

SMOTE oversampling for class imbalanced dataset introduces bias in final distribution

I have a problem statement where percentage of goods (denoted by 0) is 95%, and for bads (denoted by 1) it is 5% only. One way is to do under sampling of goods so that model understands the patterns ...
2
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0answers
25 views

Using SMOTENC in a pipeline

I am trying to figure out the appropriate way to build a pipeline to train a model which includes using the SMOTENC algorithm: Given that the N-Nearest Neighbors algorithm and Euclidian distance are ...
1
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2answers
45 views

Main options on how to deal with imbalanced data

As far as I can tell, broadly speaking, there are three ways of dealing with binary imbalanced datasets: Option 1: Create k-fold Cross-Validation samples randomly (or even better create k-fold ...
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0answers
7 views

Smote oversampling on strings

I have an imbalanced dataset with most transactions being OK and 1% transactions marked as fraud. This dataset has columns with integers (time and transaction amount and timestamp) as well as strings (...
-1
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1answer
20 views

How to increase a low recall value?

I am dealing with a HR Attrition Dataset which is highly unbalanced. I used Balancing technique like SMOTE to generate synthetic data and then used Gaussian Naive Bayes to Classify the Attrition. ...
-1
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1answer
26 views

How to best handle imbalanced text classification with Keras?

I implemented a text classification model using Keras. Most of the datasets that I use are imbalanced. Therefore, I would like to use SMOTE to handle said imbalance. I tried both on plain text, and ...
0
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1answer
58 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 ...
0
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0answers
40 views

Imblanced-data: Need assistance with SMOTE technique for a CNN input

I am trying to apply the SMOTE sampling technique to over-sample the minority class of a multiclass (5-class) problem using the convolutional neural network. As far CNN requirement, the input shape ...
0
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0answers
23 views

Imbalanced Data Classification

This is my code with which I tried to attack my imbalanced dataset from here. Now I have 2 questions. When I try to split my data into train/test data and try to run the table() function, I always ...
0
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1answer
38 views

Using MultiLabelBinarizer for SMOTE

This is my first NLP project. I'm trying to use SMOTE for a classifier with 14 classes. I need to convert the classes into an array before using SMOTE. I tried using MultiLinearBinarizer but it does ...
0
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0answers
23 views

Computing Rare values After SMOGN - Imbalanced Regression

I am dealing with a regression problem where I have the phenomenon "Imbalanced Regression". In my problem, the most relevant events are scarcely represented. In order for me to evaluate my models' ...
3
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0answers
67 views

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 ...
1
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1answer
714 views

SMOTE for regression

I am looking into upsampling an imbalanced dataset for a regression problem (Numerical target variables) in python. I attached paper and R package that implement SMOTE for regression, can anyone ...
2
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1answer
530 views

SMOTE for multilabel classification

I have a dataset with 77 different labels. Each sample has one or more of these labels. I did some data analysis and found out that the dataset is highly imbalanced - there are a large number of ...
0
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1answer
83 views

Getting feature vectors from CatBoost pool

I have a dataset with some numerical and categorical features and I am trying to apply CatBoost for categorical encoding and classification. Since my dataset is highly imbalanced, with a large number ...
3
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0answers
28 views

Unbalanced data set - how to optimize hyperparams via grid search?

I would like to optimize the hyperparameters C and Gamma of an SVC by using grid search for an unbalanced data set. So far I have used class_weights='balanced' and selected the best hyperparameters ...
2
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2answers
820 views

Passing data to SMOTE after applying train/test split

I'm trying to resample my dataset after splitting it into train and test partitions using SMOTE. Here's my code: ...
3
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2answers
383 views

Resampling with Python SMOTE

I am trying to do a simple ML re-sampling approach after the train-test split. However when I do this, it throws the below error. Can you please help me understand what this error is about? ...
1
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1answer
34 views

What are the standard parameters values for SMOTE technique?

I'm working on an imbalanced class data set (200 samples) with 2 classes, first class has 50 sample and second has 150 sample. My questions: When I use SMOTE technique on my data set my total ...
1
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1answer
46 views

What are advantages of oversampling over changing threshold for unbalanced classes?

Let's say that I have unbalanced data set that has two classes, and I am using Random Forest to make my predictions. Random forest will be biased towards the majority class, which will cause low ...
1
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1answer
101 views

Adjust predicted probability after smote

i have an imbalance data set and I used smote to oversample the minority class and undersample the majority class. now, I want to check the test AUC using predict_proba of the model. I have two ...
0
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1answer
134 views

Variation in output of Logistic Regression when using SMOTE

I am working on a logistic regression case with an imbalance in the target variable. To fix this I am using SMOTE (Synthetic Minority Oversampling Technique), but each time I run my regression model, ...
1
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1answer
656 views

How do I run SMOTE on image data using the packages available?

I need to balance some image datasets, how do I use SMOTE variants or the imblearn SMOTE method with images? I can't figure out how to, since they seem to be working only with numerical datasets.
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2answers
526 views

Oversampling/Undersampling only train set only or both train and validation set

I am working on a dataset with class imbalance problem. Now, I know one needs to oversample or undersample only the train set and not the test set. But my issue is: whether to oversample the train set ...
2
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1answer
950 views

SMOTE vs SMOTENC for binary classifier with categorical and numeric data

I have a problem that I am having trouble thoroughly understanding. I am using Xgboost for classification. My y is 0 or 1 (true or false). I have categorical and numeric features, so theoretically, I ...
3
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1answer
119 views

Combining 'class_weight' with SMOTE

This might sound a weird question, but I could not find enough details in sklearn documentation about 'class_weight'. Can we first oversample the dataset using SMOTE and then call the classifier with ...
1
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0answers
38 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 ...
1
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1answer
12 views

SMOTE for multi-instance learning i.e num_rows(x_train) > num_rows(y_train)

I have an imbalanced dataset and I wish to predict classes(0 or 1). Sample x_train: ...
2
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2answers
60 views

SMOTE on training data

The SMOTE could only be performed on the training data, so how can we do it using Weka? It means we have to put the training and test data in two separate files and run the SMOTE on the training file, ...
1
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0answers
211 views

How to apply oversampling when doing Leave-One-Group-Out cross validation?

I am working on an imbalanced data for classification and I tried to use SMOTE previously to oversampling the training data. However, this time I think I need to use a leave-on group out (LOGO) cross-...
1
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1answer
97 views

Noise Elimination with majority vote filtering

I have a dataset with label noise which I wan't to clean with majority/consensus vote filtering. This will mean I will divide the data in K-Folds and train an ensemble model. Than using the ...
1
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1answer
44 views

Class balancing of the dataset

While performing the SMOTE for balancing the class data, what should be the proportion of both class? For instance, if we have 100 instances, what (%) should be the Yes class and what should be the No ...
1
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0answers
85 views

SMOTE and oversampling with constraints

I'm trying to apply SMOTE to a dataset that has time-constraints. I have information about users visiting a website. For some features, there are time constraints, e.g having the first visit and the ...
1
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0answers
37 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 ...
4
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2answers
3k views

Why class weight is outperforming oversampling?

I am applying both class_weight and oversampling (SMOTE) techniques on a multiclass classification problem and getting better results when using the class_weight technique. Could someone please ...
0
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2answers
60 views

Forcing class imbalance to mirror the target data

I'm trying to do binary classification on some data, my source data has a class split of 40% A / 60% B while my target data has a split of 70% A / 30% B. Is it a worthwhile strategy to use SMOTE to ...
0
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0answers
89 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 ...
3
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1answer
1k views

SVM SMOTE fit_resample() function runs forever with no result

Problem fit_resample(X,y) is taking too long to complete execution for 2million rows. Dataset specifications I have a labeled dataset about network features, ...
1
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1answer
2k views

Using SMOTE for Synthetic Data generation to improve performance on unbalanced data

I presently have a dataset with 21392 samples, of which, 16948 belong to the majority class (class A) and the remaining 4444 belong to the minority class (class B). I am presently using SMOTE (...
3
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1answer
357 views

Should I oversample my validation data to get better F1 score and PRC?

I am currently working with a dataset that is imbalanced, about 30k rows * 14 features (just for you know), and 99.5% of the data is labeled 0. Since the model is strongly imbalanced I decided to use ...
3
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1answer
2k views

PCA, SMOTE and cross validation- how to combine them together?

I was reading a lot recently about PCA and cross validation and it seems that the majority call it malpractice to do PCA before cross validation. I would also like to perform SMOTE, but there is a ...
0
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2answers
1k views

solving multi-class imbalance classification using smote and OSS

I am trying to solve multi-class imbalance classification problem for that i am using SMOTE for oversampling and OSS for under-sampling. But I have a doubt as I am working on multi-class so i have to ...
1
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0answers
34 views

SMOTE caused my total nrows in train to fall to a very small proportion

I have a highly skewed dataset with minority class in target being just about 4%. I decided to apply SMOTE using library DMwR in R. Here is my target: ...
1
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0answers
20 views

SmoteBoost: Should SMOTE be ran individually for each iteration/tree in the boosting?

As per the paper on SmoteBoost, SMOTE is ran for each iteration of the boosting, generating N samples, which are further added to the original training data and the weight distribution of the ...
0
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1answer
66 views

Methods for augmenting binary datasets

I have a small (~100 samples) dataset with roughly 20 features which are mostly binary, and a few are numeric (~5). I wanted to use methods for augmenting the training set and see if I can get better ...
1
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0answers
205 views

Increase Specificity of a model using SMOTE arguments from DMwR package in R when training data is unbalanced

I'm working on a binary classification problem and training data which I'm using is unbalanced. I used SMOTE function from DMwR ...
0
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2answers
60 views

How to make multiple regression perform better for outliers? (without reducing effect of them)

I have a small dataset(about 60 samples) and I need it to predict well for high target values. There are only a few high values and all models I tried perform poorly for these high values. So I ...
2
votes
1answer
283 views

Optimizing decision threshold on model with oversampled/imbalanced data

I'm working on developing a model with a highly imbalanced dataset (0.7% Minority class). To remedy the imbalance, I was going to oversample using algorithms from imbalanced-learn library. I had a ...
1
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0answers
99 views

A classification machine learning flow chart implimenting dimentionality reduction, upsampling, and cross validation [closed]

I would like to make a flow chart for an ML classifier and make sure that my thinking is correct. Here is a little about my sample: I have 3 classes and about 160 features. I suspect that some of ...
0
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1answer
538 views

Vertical and horizontal lines appearing on large confusion matrix?

I have produced a large heatmap-like confusion matrix and am seeing horizontal and vertical lines on it, so I'm trying to determine: What they mean Why they are there How I can improve on this ...