Noisy data

Noisy data presents a significant challenge in machine learning because it can lead to biased or inaccurate model training. The presence of outliers, incorrect labels, or missing values can distort the underlying patterns in the data, causing models to learn spurious correlations or fail to generalize well to unseen data. Various techniques are employed to mitigate the effects of noisy data, including data cleaning, outlier detection, and robust statistical methods. Data cleaning involves identifying and correcting or removing errors and inconsistencies in the data. Outlier detection methods aim to identify and remove data points that deviate significantly from the norm. Robust statistical methods are designed to be less sensitive to the presence of noisy data. Addressing noisy data is crucial for building reliable and accurate machine learning models, as the quality of the data directly impacts the quality of the model.