This is a type of data augmentation for tabular data and can be very effective.Perhaps the most widely used approach to synthesizing new examples is called the SMOTE works by selecting examples that are close in the feature space, drawing a line between the examples in the feature space and drawing a new sample at a point along that line.Specifically, a random example from the minority class is first chosen.
Imbalanced classification involves developing predictive models on classification datasets that have a severe class imbalance.The challenge of working with imbalanced datasets is that most machine learning techniques will ignore, and in turn have poor performance on, the minority class, although typically it is performance on the minority class that is most important.One approach to addressing imbalanced datasets is to oversample the minority class. Instead, new examples can be synthesized from the existing examples.
I’d like to do some research/experiment on it in the meantime.Thank you!No, in general I rather make recommendations after doing my homework.Thank you for the great description over handling imbalanced datasets using SMOTE and its alternative methods.
Intuitions breakdown in high dimensions, or with ml in general.
Can you please help me how to do sampling.
What should be done to implement oversampling only on the training set and we also want to use stratified approach?Correct, and we do that later in the tutorial when evaluating models.In the first example I am getting you used to the API and the affect of the method.Can you please refer that tutorial to me where we we are implementing smote on taining data only and evaluating the model? It is not a time series. )First of all, thanks for the response. Is this correct?Correct. I have encountered an error when running“ValueError: The specified ratio required to remove samples from the minority class while trying to generate new samples. After making balanced data with these thechniques, Could I use not machine learning algorithms but deep learning algorithms such as CNN?Yes, but it is called data augmentation and works a little differently:I’ve used data augmentation technique once. I used data from the first ten months for training, and data from the eleventh month for testing in order to explain it easier to my users, but I feel that it is not correct, and I guess I should use a random test split from the entire data set.
I have only feature for categorization.
SMOTE for Balancing Data.
Imagine that you are a doctor.
What about if you wish to increase the entire dataset size as to have more samples and potentially improve model?Thank you for the great tutorial, as always super detailed and helpfull. Is it true ?Hmmm, that would be my intuition too, but always test.
First, we can use the make_classification() scikit-learn function to create a synthetic binary classification dataset with 10,000 examples and a 1:100 class distribution. This can balance the class distribution but does not provide any additional information to the model.An improvement on duplicating examples from the minority class is to synthesize new examples from the minority class. Generally, you need to experiment with a few of them before deciding on one.
if so, what is any preprocessing/dimensionality reduction required before applying SMOTE?Not sure off the cuff, perhaps experiment to see if this makes sense.hi Jason , I am having 3 input Text columns out of 2 are categorical and 1 is unstructured text.
Test everything.You can use it as part of a Pipeline to ensure that SMOTE is only applied to the training dataset, not val or test.Let’s say you train a pipeline using a train dataset and it has 3 steps: MinMaxScaler, SMOTE and LogisticRegression.Can you use the same pipeline to preprocess test data ?How does pipeline.predict(X_test) that it should not execute SMOTE ?The pipeline is fit and then the pipeline can be used to make predictions on new data.Yes, call pipeline.predict() to ensure the data is prepared correctly prior to being passed to the model.Hi Jason, SMOTE sampling is done before / after data cleaning or pre-processing or feature engineering??? The dataset currently has appx 0.008% ‘yes’.I came across 2 method to deal with the imbalance. You can also view it There are many sampling techniques for balancing data.
Output column is categorical and is imbalanced.Perhaps use a label or one hot encoding for the categorical inputs and a bag of words for the text data.You can see many examples on the blog, try searching.I have used Pipeline and columntransformer to pass multiplecolumns as X but for sampling I ma not to find any example.For single column I ma able to use SMOTE but how to pass more than in X?You may have to experiment, perhaps different smote instances, perhaps run the pipeline manually, etc.Perhaps try and get more examples from the minority class?Many thanks for this article. Recently I was working on a project where the data set I had was completely imbalanced.