Outliers, which are extreme data points in a dataset, can substantially impact prediction results in data analysis and machine learning. They can skew models, reduce overall model performance, and lead to inaccurate parameter estimations.

Example

Let's examine a simple linear regression model applied to a dataset containing numerous outliers, and then compare its performance when applied to a cleaned dataset to observe the disparities.

Let's discover some key differences:

• The presence of outliers in the dataset significantly impacts the linear regression model's performance, leading to a less accurate representation of the underlying relationship;
• Removing outliers improves the model's performance, resulting in a better fit to the true linear relationship;
• The coefficients of the regression models also differ, highlighting how outliers can affect parameter estimation.

Do outliers always indicate errors in the data?

Occasionally, when we see unusual data points that stand out, they might signal a significant change in the data itself or point to shifts in trends and patterns. Let's examine this idea using an example:

Imagine you have a dataset showing daily stock prices over several years. Usually, these stock prices follow a consistent pattern. However, when a financial crisis occurs, there may be sudden and significant price drops due to market panic and economic challenges.

It's important to note that these dramatic stock price drops during a financial crisis are not just regular outliers; they are contextual anomalies that are closely tied to the unique circumstances of the crisis. These anomalies are crucial for our analysis and need to be considered.