Data Quality Checklist

Why data quality matters for Machine Learning

In order for machine learning to be effective, the data that is used to train the algorithms must be of high quality. If the data is noisy or contains errors, the machine learning models will be less accurate. Data quality is especially important for supervised learning, where the data is used to train the model and then make predictions. If the training data is of low quality, the model will not be able to learn from it and will not be able to make accurate predictions. There are many ways to improve the quality of data, such as pre-processing the data to remove noise or outliers, or using data augmentation to create more data. However, it is important to remember that no matter how much data is available, it will be of little use if it is of poor quality.

Why a Checklist

If you're not using a checklist when you're doing machine learning, you're missing out on a key tool that can help improve the quality of your results. Checklists help you to identify and avoid potential errors, and can be a valuable aid in ensuring that your machine learning models are as accurate as possible.