AI-Accelerated Product Development
Working with imbalanced classification (binary case)
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The user of this short list will learn about the different techniques to handle imbalanced datasets and the importance of model calibration
Potential Use Cases
You are working with imbalanced datasets, for example fraud detection.
Who is This For ?
Practitioners working with imbalanced datasets
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to see details.
1. A Gentle Introduction to Imbalanced Classification
What is a classification problem?
Why imbalanced datasets are a challenge for classification algorithms?
Which ones are examples of imbalanced classification problems?
2. Machine Learning Classification How to Deal with Imbalanced Data
Why it is important to deal with imbalanced data?
How to use SMOTE?
3. 7 Techniques to Handle Imbalanced Data
Which evaluation metrics can be used for an imbalanced dataset?
How can the dataset be resample?
Which techniques can be applied to imbalanced datasets?
4. Dealing with Imbalanced Data
Which metrics are used with imbalanced datasets?
How to oversample the data?
How to under sample the data?
5. Classifier calibration
Why calibration is important?
How to create a probability density plot of your model?
How to calibrate the model?
6. How to Calibrate Probabilities for Imbalanced Classification
Why uncalibrated probabilities are a problem?
How to calibrate probabilities?
How to calibrate SVM?
How to calibrate KNN?