Importance of Detecting Outlier
Machine learning models and statistical analysis are susceptible to major disruptions from outliers. In statistical analysis, for instance, anomalies have the potential to distort the mean and standard deviation, resulting in imprecise estimations of central tendency and variability. Outliers in machine learning models can skew the findings by exerting an excessive amount of influence on the model’s predictions.
Outlier Detection is an important process in identifying the patterns and the “story” a dataset holds. Some of the important significance of Outlier Detection is as follows:
- Assuring data quality: The presence of Outlier in a dataset is indicative of measurement in error or rare events. Identifying and addressing these outliers helps in maintaining data integrity and quality
- Model performance : The presence of global outliers can have a significant impact on model performance. Outliers can reduce the effectiveness of predictive models and lead to poor generalization. Identifying them helps in getting more accurate results
- Inference and Hypothesis Testing: It is important to detect Outliers so as to avoid making any incorrect hypothesis about the data and validate our inferences
- Domain Specific Insights: In many industrial aspects, Outlier can be used as rare events occuring and further enhance the pattern recognition and trend generation. For example, Outliers have great impact in risk assessment of Trading or Financial modellings.
Understanding Global Outliers
An outlier is a data point that differs significantly from other data points. This significant difference can arise due to many circumstances, be it an experimental error or mistake or a difference in measurements. In this article, we will review one of the types of outliers: global outliers.
In data analysis, it is essential to comprehend and recognize global outliers. Understanding the overall distribution of the data and spotting any outliers both depend heavily on visual inspection. Additionally, the visualization sheds light on the potential effects of outliers on the relationship between characteristics and the target variable.