Imbalanced data is a common problem in machine learning where the number of samples in each class is not equal. This can lead to biased models that are over-representing one class and under-representing another. One of the ways to handle imbalanced data is to use resampling techniques that modify the class distribution in the dataset. SMOTE (Synthetic Minority Over-sampling Technique) and Near Miss algorithm are two popular techniques to handle imbalanced data.
In this article, we will discuss how to use SMOTE and Near Miss algorithm in Python to handle imbalanced data.
SMOTE (Synthetic Minority Over-sampling Technique) :
SMOTE is a popular technique for oversampling the minority class. The SMOTE algorithm works by generating synthetic samples of the minority class by interpolating between existing minority class samples. The idea behind SMOTE is to create new samples that are similar to the existing minority class samples but slightly different.
In Python, the imbalanced-learn library provides an implementation of SMOTE. To use SMOTE, first, we need to import the SMOTE class from the imblearn.over_sampling module. Then we can create an instance of the SMOTE class and use the fit_resample method to resample the data.
The fit_resample method takes two arguments, the features (X) and the target variable (y). It returns two arrays, X_resampled and y_resampled, which contain the resampled data.
Near Miss Algorithm :
The Near Miss algorithm is an undersampling technique that removes samples from the majority class that are close to the minority class. The idea behind the Near Miss algorithm is to remove samples that are not useful for classification.
In Python, the imbalanced-learn library provides an implementation of the Near Miss algorithm. To use the Near Miss algorithm, we need to import the NearMiss class from the imblearn.under_sampling module. Then we can create an instance of the NearMiss class and use the fit_resample method to resample the data.
The fit_resample method takes two arguments, the features (X) and the target variable (y). It returns two arrays, X_resampled and y_resampled, which contain the resampled data.
Comparing SMOTE and Near Miss Algorithm :
SMOTE and the Near Miss algorithm are two different resampling techniques that can be used to handle imbalanced data. SMOTE is an oversampling technique that creates new samples in the minority class by interpolating between existing samples. On the other hand, the Near Miss algorithm is an undersampling technique that removes samples from the majority class that are close to the minority class.
SMOTE is often a good choice when the minority class is too small, and the existing samples are not enough to create a representative model. On the other hand, the Near Miss algorithm is a good choice when the majority class has a lot of redundant samples that do not add much to the model's accuracy.
Conclusion :
Handling imbalanced data is an essential part of building a machine learning model. SMOTE and the Near Miss algorithm are two popular techniques for handling imbalanced data. In this article, we discussed how to use SMOTE and the Near Miss algorithm in Python using the imbalanced-learn library. SMOTE is an oversampling technique that creates synthetic samples in the minority class, while the Near Miss algorithm is an undersampling technique that removes samples from the majority class that are close to the minority class.
It is important to note that both SMOTE and the Near Miss algorithm are not perfect solutions to handle imbalanced data. Resampling techniques should be used in combination with other techniques such as feature engineering, hyperparameter tuning, and ensemble methods to build an accurate and robust model.
In conclusion, handling imbalanced data is a critical step in building a machine learning model that can accurately classify data from different classes. SMOTE and the Near Miss algorithm are powerful tools that can help to address the problem of imbalanced data. By using these techniques in Python with the imbalanced-learn library, we can create a more balanced dataset that can lead to more accurate and robust models.
