- Winsorize (cap at threshold).
- Transform to reduce skew (using Box-Cox or similar).
- Remove outliers if you’re certain they are anomalies or measurement errors.
There are several data preprocessing techniques to handle outliers in machine learning. Here are three commonly used ones:
- Removing outliers: One straightforward approach is to remove the data points that are identified as outliers. This can be done using statistical methods such as Z-score, where data points that fall beyond a certain number of standard deviations from the mean are considered outliers and removed. Another method is using the Interquartile Range (IQR) where outliers are defined as data points that fall below Q1 – 1.5 * IQR or above Q3 + 1.5 * IQR, and then removing those outliers.
- Transforming the data: Another approach is to transform the data to make it more normally distributed, as many machine learning algorithms assume normality. Techniques like logarithmic transformation, square root transformation, or Box-Cox transformation can be used to reduce the impact of outliers and make the distribution more symmetric.
- Binning or discretization: Binning involves dividing the entire range of values into a series of intervals or bins and then replacing the actual values with the bin they fall into. This can help to mitigate the effect of outliers by reducing the range of values and making the data more robust. However, this approach may result in information loss, so it should be used judiciously.
It’s important to note that the choice of preprocessing technique depends on the nature of the data, the specific machine learning algorithm being used, and the problem at hand. Additionally, it’s crucial to handle outliers carefully as removing or transforming them incorrectly can lead to biased models or loss of valuable information.