How do you deal with class imbalance in NLP?
The simplest way to fix imbalanced dataset is simply balancing them by oversampling instances of the minority class or undersampling instances of the majority class. Using advanced techniques like SMOTE(Synthetic Minority Over-sampling Technique) will help you create new synthetic instances from minority class.
What are the major challenges issues of machine learning?
Table of contents
- Not enough training data :
- Poor Quality of data:
- Irrelevant Features:
- Nonrepresentative training data:
- Overfitting and Underfitting :
Do you have a problem with class imbalance?
If you have had the chance of working around classification problems, then is probable you have faced a problem of imbalanced classes.
How does class imbalance affect machine learning classification?
So when we have a class imbalance, the machine learning classifier tends to be more biased towards the majority class, causing bad classification of the minority class. The Accuracy Paradox refers to the utility of using the Accuracy out of our Confusion Matrix as a metric for predictive modelling when classifying imbalanced classes.
Are there any imbalanced classes in the data?
The dataset is high imbalanced, with only 0.17% of transactions being classified as fraudulent. The full notebook can be found here. Our objective will be to correctly classify the minority class of fraudulent transactions.
How to randomly duplicate observations from a minority class?
Up-sampling is a simple process about randomly duplicating observations from a minority class. You can import the resample module from sklearn.utils ( there’s also available the RandomUnderSampler module from imblearn.under_sampling ), separate observations from the minority class into a new DataFrame and run the following code: