Description: Outlier removal is the process of identifying and removing data points that significantly deviate from the rest of a dataset. These outliers can arise for various reasons, such as measurement errors, natural variations in the data, or unusual experimental conditions. Their presence can distort the results of statistical analyses and machine learning models, affecting the accuracy and validity of conclusions. In the context of supervised learning, where the goal is to build predictive models from labeled data, outlier removal is crucial for improving model quality. By eliminating these extreme data points, better model generalization can be achieved, as it is trained on data that is more representative of the target population. Additionally, identifying outliers can provide valuable insights into the phenomenon being studied, allowing analysts to better understand variations in the data. In summary, outlier removal is an essential practice in data preprocessing that contributes to the robustness and effectiveness of data analysis and modeling processes.
