SMOTE for oversampling minority class in an Imbalanced Dataset

SMOTE stands for Synthetic Minority Oversampling Technique. It uses k-nearest neighbors of the minority class to create a synthetic row for the class. We can specify how much data to synthesize by mentioning the ratio to the majority class. We can also specify which classes to resample - majority, minority, not majority, not minority, all classes.

There are other variants of SMOTE in which minority class is synthesized using SVM algorithm or KMeans clustering.


Python Implementation : pip install imbalanced-learn ; from imblearn.over_sampling import SMOTE ; <SMOTE object>.fit_resample(X,Y)


Comments

Post a Comment

Popular posts from this blog

Principal Component Analysis (PCA)

Principal Component Analysis (PCA) is a dimentionality reduction technique. It is an unsupervised learning algorithm. Dimentionality refers to the number of input features or columns in a dataset. PCA reduces the number of input features in the model by grouping them together to create new features while preserving as much information as possible. The number of new features (components) of PCA is equal to the number of input features. The reasons for using this technique are - Provide smaller set of input features to the model, after having removed unwanted columns and columns having no effect on the output Group columns which are redundant, highly correlated and depict the same underlying concept. Having these extra columns leads to overfitting and unnecessary complexity. For model regularization. PCA does lead to some information loss while reducing the features but it can make the model simpler to understand and increases validation accuracy PCA uses covariance matrix. The first co...
Read more

Transfer Learning

Transfer learning refers to using pre-trained deep learning models in similar datasets to which it was trained on. Pre-trained deep learning models have weights (or coefficients or parameters) determined beforehand by training and optimizing them on extremely huge datasets. These models have some of the highest test accuracy% for the specific dataset types they have been trained on and have been the de-facto standard, state-of-the-art (SOTA) models when they were introduced first. These models are built using a variation of either Convolutional neural nets (CNNs) or Recurrent neural nets (RNNs). CNNs and RNNs work extremely well, thus, mostly used on datasets that have correlated patterns amongst nearby input values e.g. images, languages, music, videos, time-series and text data. Few of the examples are - VGG16, ResNet50, InceptionV3 for images; BERT, GPT-2, XLNet, ELMO for NLP. Typically these CNNs and RNNs consist of numerous training layers, with each layer consisting of multiple n...
Read more

Receiver Operating Characteristic (ROC) Curve

Image
Receiver Operating Characteristic (ROC) curve is used for the analysis of results from a classification model. A classification model outputs probabilities. A cutoff decides whether to classify a probability as a positive or a negative output. By default, this is assumed to be 0.5. So, a probability below 0.5 belongs to negative class and greater equal-to 0.5 belongs to positive class. But this is an assumption. An ROC curve helps us play with this assumption to best suit the problem at hand i.e. whether we want to decrease or increase the number of positives from the prediction. This cutoff is tuned between 0 and 1 to plot two metrics in an ROC curve, namely - True Positive Rate (TPR) and False Positive Rate (FPR). Only TPR and FPR values are plotted on the graph. Corresponding probability cutoffs are present separately in a table. Now coming to the concept of TPR and FPR - For all predicted positives, they can be either true (right) or false (wrong). For all predicted negatives, they...
Read more