Datasets are inherently messy, and with this type of disorder IT professionals must inspect datasets to maintain data quality. Increasingly, models power business operations, so IT teams are protecting machine learning models from running with imbalanced data.
An imbalanced dataset is a situation in which a predictive classification model incorrectly identifies an observation as a minority class. This occurs when observations are tested for the classification designed by the model, but the test includes so few observations that the model operates with a skewed prediction accuracy.
For example, consider a company that examines data from 100 samples of a product. Suppose a model built on that data predicted that 90 would meet the desired quality threshold score, and 10 would not. That model would have 90% accuracy for selecting products that meet that score. This accuracy, however, assumes that proportion of the terms as a fixed bet hold strongly for the next dataset to which the model is applied.
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The result of that "sure condition" is a biased model with a false sense of data identity. The model misidentifies observations from a large dataset, and misidentification scales given dataset size.
high-dimensional dataset
The situation gets worse with higher-dimensional datasets. These datasets contain many variables, with the number of variables exceeding the number of observations in some instances. That layout of the data - a detailed table of variables with some comments - is the same size as the 90/10 example, with the important difference of having more features (variables). High dimensionality can affect a model with a bias towards the majority class.
Such bias can have social consequences, such as facial recognition systems that do not identify black faces well from images. These systems have been criticized for perpetuating discrimination and racism because their biases can lead to illegal arrests and false criminal charges by officers.
Retail operations offer real-world examples of common business impacts from unbalanced data. A customer database in which a minority of customers may unsubscribe from a service may affect how a model measures customer churn for products and services. Fraudulent purchases or returns are additional examples where the minority may be too small to detect.
The most straight-forward solution to an unbalanced dataset is to collect more data, but additional data collection is not an option in every instance. The observations that make up the dataset may be limited due to some event or other practical consideration. An unexpected cut in product production – as experienced last year due to COVID-19 – is a good example.
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use imputation
A different solution is to use impersonation. Imputation is a process of assigning a value to missing data by estimation. There are some changes to the accrual process. One imprecise option is data resampling. In resampling, analysts can do one of two things:
Add copies of the underrepresented class, which is called oversampling.
Remove observations from the overrepresented class, which is called undersampling.
Either option means to correct for the effect of dataset features, reducing bias in the model.
An advanced imputation technique is the Synthetic Minority Over-sampling Technique (SMOTE). SMOTE creates synthetic samples calculated from short squares, rather than the repetitions or adjustments used in reproduction. This provides more observations without adding features that may negatively inform the model. SMOTE applies a nearest neighbor vector calculation on a pair of minority class observations, then builds additional observations from that calculation. The oversampling process is repeated until all observation pairs have been evaluated with the nearest neighbor count.
There are libraries in R and packages for Python designed to implement SMOTE within a program. No matter which programming language you decide to use, there is a general approach that can be taken to examine a dataset for potential imbalances. First, select the observations that are in the training set for the model. Next, create a summary line in the program to verify that the instance classes were created. The final step is a quality assurance step, making scatterplots to see if the classes make intuitive sense.
Other ways to observe class imbalance in the data are through examining the results of machine learning models. Analysts can look at the performance of a single model or compare the output of multiple models on the same data to note which model best classifies and treats the minority in output. One technique, called the punitive model, imposes a cost on the model for making mistakes in classes. This helps to know which models are likely to have the most devastating effects from a decision.
The key point is to develop a comparison of datasets before and after the imputation process. data analysts
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