Why is data cleaning important in machine learning?

Data cleaning is essential in machine learning because the quality of the data directly impacts the accuracy and effectiveness of the model. When data is unclean or contains errors, biases, duplicates, or inconsistencies, it can lead to inaccurate predictions and unreliable results. By cleaning and preprocessing the data, machine learning models are able to make more accurate and reliable predictions, ultimately improving the overall performance and efficiency of the system. Furthermore, clean data ensures that the model is able to generalize well and make more informed decisions based on the patterns and insights within the data.

This mind map was published on 28 June 2024 and has been viewed 65 times.