Here we will use Again, it’s important to generate the new samples only in the training set to ensure our model generalizes well to unseen data.After generating our synthetic data points, let’s see how our logistic regression performs.Our F1 score is increased and recall is similar to the upsampled model above and for our data here outperforms undersampling.We explored 5 different methods for dealing with imbalanced datasets:It appears for this particular dataset random forest and SMOTE are among the best of the options we tried here.These are just some of the many possible methods to try when dealing with imbalanced datasets, and not an exhaustive list. Imbalanced classes are a common problem in machine learning classification where there are a disproportionate ratio of observations in each class. That said, there should not be any big impact on your model performance if the level of imbalance is relatively low.Now, let’s cover a few techniques to solve the class imbalance problem.Over-sampling increases the number of minority class members in the training set. Since it is removing observations from the original data set, it might discard useful information.Identifying these features will help us generate a clear decision boundary with respect to each class. For sample dataset, refer to the References section. You get 95% accuracy but your model in predicting wrong every time? This tells us that either we did something wrong in our logistic regression model, or that accuracy might not be our best option for measuring performance.Let’s take a look at some popular methods for dealing with class imbalance.As we saw above, accuracy is not the best metric to use when evaluating imbalanced datasets as it can be very misleading. A few approaches that help us in tackling the problem at the data point level are undersampling, oversampling, and feature selection. Oversampling can be a good choice when you don’t have a ton of data to work with.We will use the resampling module from Scikit-Learn to randomly replicate samples from the minority class.Always split into test and train sets BEFORE trying oversampling techniques! Metrics that can provide better insight include:Let’s see what happens when we apply these F1 and recall scores to our logistic regression from above.These scores don’t look quite so impressive. The goal of this technique is mainly to pursue a high accuracy of classifying examples into a set of known classes. Some others methods to consider are collecting more data or choosing different resampling ratios — you don’t have to have exactly a 1:1 ratio!You should always try several approaches and then decide which is best for your problem.Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. This could lead to underfitting and poor generalization to the test set.We will again use the resampling module from Scikit-Learn to randomly remove samples from the majority class.Again, we have an equal ratio of fraud to not fraud data points, but in this case a much smaller quantity of data to train the model on. Let’s see what other methods we might try to improve our new metrics.While in every machine learning problem, it’s a good rule of thumb to try a variety of algorithms, it can be especially beneficial with imbalanced datasets.
Let’s compare this to logistic regression, an actual trained classifier.Maybe not surprisingly, our accuracy score decreased as compared to the dummy classifier above. It is playing as one of the important roles in the machine learning algorithms including the real-world data mining applications.The ensemble-based method is another technique which is used to deal with imbalanced data sets, and the ensemble technique is combined the result or performance of several classifiers to improve the performance of single classifier. The advantage of over-sampling is that no information from the original training set is lost, as all observations from the minority and majority classes are kept. There are various approaches in ensemble learning such as Bagging, Boosting, etc.Imbalanced data is one of the potential problems in the field of data mining and machine learning. Change the algorithm. Let’s say you are working in a leading tech company and company is giving you a task to train a model on detecting the fraud detection. This helps the models to classify the data more accurately. But a drawback is that we are removing information that may be valuable. Undersampling can be a good choice when you have a ton of data -think millions of rows. But here’s the catch. Let’s try one more method for handling imbalanced data.A technique similar to upsampling is to create synthetic samples. We can say that the number of positive values and negative values in approximately same.Imbalanced data is not always a bad thing, and in real data sets, there is always some degree of imbalance.
This problem can be approached by properly analyzing the data.
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