All Questions
Tagged with class-imbalance predictive-modeling
20 questions
-1
votes
2
answers
32
views
Imbalanced class in my dataset
I’m working with an imbalanced dataset to predict strokes, where the positive class (stroke occurrence) is significantly underrepresented. Initially, I used logistic regression, but due to the class ...
0
votes
0
answers
16
views
Impact of Adding Imbalanced Data on Model Performance for Different Groups
Suppose I initially have a dataset with 50 samples of type A and 50 samples of type B, each with several features. I built a neural network model using this data and recorded the prediction accuracy ...
- 1
5
votes
1
answer
587
views
Are imbalanced data problems solvable? [closed]
I am working as a data scientist for the past 2 years where I have worked on problems related to binary classification, revenue prediction etc.
In the past two years, I have had 2 problems that ...
- 2,655
2
votes
3
answers
109
views
Measuring performance of customer purchase predictions
My goal is to develop a model that predicts next customer purchases in USD (Update: During the time period of the dataset, if no purchase was made by the customer, the next purchase label is set to ...
1
vote
1
answer
31
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Remedie for a stubborn recall result?
I was working on a project connected to predicting default on credit loan with 0-1 loss. The recall is a crucial measure that should be maximized in this case, while monitoring precision for sanity of ...
0
votes
1
answer
81
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Low leves of probability observed after modelling.Is it right to scale the probability
I have done modelling on imbalanced class , without any sampling methods.
Event rate is around 0.1 ,After modelling I am getting probalities more at the lower side close to zero.I have tried differnt ...
- 1
4
votes
1
answer
1k
views
Why is oversampling outperforming class weight?
I have a dataset that is highly imbalanced. One class has 412 (class 0) samples while the other has 67215 (class 1) samples. For its classification, I am using MLP. When I use class weight of 165 for ...
- 1,161
1
vote
1
answer
52
views
Sequence to carry out data analysis?
I have a dataset with 4700 records and it's a classification problem. Proportion of classes is 33 and 67%
few questions
1) does this proportion qualify dataset as imbalanced ?
2) should I do ...
- 2,655
3
votes
2
answers
390
views
Does Sampling size matters in Multi classification Model
I am working on a multi class classification model where few of the class are with less data compare to other classes.
I used random sampling technique to create a sample from the population keeping ...
- 788
6
votes
1
answer
5k
views
Difference between sklearn make_pipeline and imblearn make_pipeline
Can anybody please explain the difference between sklearn.pipeline.make_pipline and imblearn.pipeline.make_pipline.
- 161
8
votes
2
answers
110
views
Which classification algorithms are negatively affected by class imbalances?
I've seen a few posts and papers floating around the web (mostly those related to over/undersampling, SMOTE, and cost-sensitive training) that, when discussing class imbalance, specify that certain ...
5
votes
3
answers
3k
views
Why does balancing the test dataset improve precision-recall curve?
I have a fairly imbalanced dataset for default-risk credit scoring (2:98). Both costs are fairly important i.e False negative means loss from default and false positive is a lost-revenue opportunity.
...
- 371
0
votes
2
answers
478
views
Can we make two separate models vs one for classification?
Suppose I have a binary classification problem and my data is imbalanced, I can build a classification model using any of the algorithms and use an oversampling or undersampling technique to handle ...
2
votes
2
answers
5k
views
Random Forest Classifier Probabilities
My dataset has 140k rows with 5 attributes and 1 Attrition as target variable (value can either be 0 (Customer churn) or 1 (Customer Does not churn)). I divided my dataset in 80% training and 20% ...
- 243
5
votes
3
answers
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 ...
- 53
2
votes
1
answer
8k
views
How to improve precision under imbalanced classification
I am using an imbalanced dataset (rare positive cases) to learn models for prediction and the final good AUC is 0.92 but the F1 score is very low0.2.
Is it possible to add some key features which ...
- 783
-1
votes
1
answer
33
views
Time-based over-sampling dilemma
Background:
I'm working on a binary classifier that tries to predict when -- if ever -- a user goes bad, a terminal state from which a user cannot recover. This phenomenon is tricky becuase a user ...
- 4,189
1
vote
3
answers
888
views
How to compensate for class imbalance in prediction model?
I'm trying to run a prediction model on a customers' data set to predict the likelihood that a new customer would be interested in buying product X, offered by a company that sells products X,Y and Z. ...
- 11
6
votes
3
answers
2k
views
Balanced Train set to predict Imbalanced Prediction set
One of the methods to address a classification predictive analysis on an imbalanced set consist on undersample the majority class (others approaches consist on: undersample the majority class, ...
- 146
3
votes
1
answer
216
views
Modeling when the response variable has too many 0's and few continuous values?
For problems where the data represents online fraud or insurance (where each row represents a transaction), it is typical for the response variable to denote the value of fraud committed in dollars. ...
- 1,615