Description: The ‘Robust Scaler’ is a data preprocessing technique used to scale features in datasets, especially those where the presence of outliers can distort results. Unlike traditional methods that use the mean and standard deviation, the robust scaler relies on statistics that are less sensitive to these extreme values, specifically the median and the interquartile range. This means that, when applying this technique, the goal is to maintain the integrity of the data and ensure that the scaled features more accurately reflect the underlying distribution of the data. The median provides a central value that is unaffected by extremes, while the interquartile range measures the dispersion of the data around this central value, allowing for more effective normalization. This technique is particularly useful in the fields of machine learning and data mining, where the quality of input data is crucial for model performance. By using the robust scaler, analysts can improve the stability and accuracy of their models, resulting in more reliable predictions and a better understanding of patterns in the data.
