{"id":375,"date":"2022-12-30T00:00:00","date_gmt":"2022-12-30T00:00:00","guid":{"rendered":"https:\/\/tac.debuzzify.com\/?p=375"},"modified":"2023-06-27T03:44:56","modified_gmt":"2023-06-27T03:44:56","slug":"how-to-check-if-data-is-imbalanced","status":"publish","type":"post","link":"https:\/\/www.the-analytics.club\/how-to-check-if-data-is-imbalanced\/","title":{"rendered":"How to Check if a Dataset Is Imbalanced"},"content":{"rendered":"\n\n\n
A dataset is imbalanced if the classes within the dataset are not evenly distributed.<\/p>\n\n\n\n
For example, if you are building a machine learning model to classify whether an email is spam, and 99% of the emails in your dataset are not spam, then your dataset is imbalanced.<\/p>\n\n\n\n
A spam classification is a relatively easygoing example. But ML models trained on the imbalanced dataset can have more severe consequences.<\/p>\n\n\n\n
For example, if a machine learning model is trained on a dataset<\/a> where most patients do not have a specific disease, it may be less accurate at predicting the disease in patients with it. This could lead to misdiagnosis or inadequate treatment.<\/p>\n\n\n\n Let’s take another example. Suppose an ML model is used to predict the likelihood of a defendant reoffending, and the dataset is imbalanced with many non-offenders; the model may be biased towards predicting a low risk of reoffending. This could result in lenient sentences for high-risk individuals and unfairly harsh sentences for low-risk individuals.<\/p>\n\n\n\n Finally, imbalanced datasets can also have broader social impacts. For instance, if a machine learning model<\/a> is used to predict which job candidates are most likely to succeed, and the dataset is imbalanced with a majority of successful candidates coming from a specific group (e.g., men, a particular racial group, etc.), the model may be biased towards predicting success for candidates from that group. This could perpetuate existing societal biases and contribute to unequal opportunities.<\/p>\n\n\n\n It is essential to be aware of the potential impacts of imbalanced datasets and take steps to address them when building machine learning models. This article focuses on various techniques to identify imbalanced datasets.<\/p>\n\n\n\n Grab your aromatic coffee <\/a>(or tea<\/a>) and get ready…!<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n You can visually inspect the distribution of classes in your dataset by plotting a bar chart or histogram. If one type is significantly larger than the other, then your dataset is likely imbalanced.<\/p>\n\n\n\n Use a library such as Matplotlib<\/a> to create a bar chart or histogram to visualize the class distribution in your dataset. For example:<\/p>\n\n\n\nUse Visual inspection to find imbalanced datasets.<\/h2>\n\n\n\n