What is outliers?
Data points that are significantly different from other data points in a dataset
outliers explained in plain English
Outliers are values in a dataset that are far away from the average or expected values. They can be very high or very low and often indicate unusual or extraordinary events.
Analogy
Outliers are like unusual guests at a party, they stand out from the crowd and can affect the overall atmosphere or outcome.
Example
A company's sales data may show an outlier for a particular day when sales were much higher than usual due to a special promotion or holiday.
How is outliers used?
Outliers are used to identify unusual patterns or errors in data, and to prevent them from affecting the accuracy of statistical models or machine learning algorithms.
Common misconceptions about outliers
Some people think that outliers are always errors or mistakes, but they can also be legitimate data points that provide valuable insights.
History
The concept of outliers has been around for centuries, but it gained significant attention in the 20th century with the development of statistical methods and data analysis techniques.
People also read
- A/B testing
A method of comparing two versions of a product or service to determine which one performs better
- ablation
A technique used to remove or disable parts of a machine learning model to understand their importance
- accuracy
The degree to which a model's predictions match the actual outcomes
- activation function
A mathematical function that introduces non-linearity into a neural network model
- active learning
A machine learning approach where the model actively selects the most informative data to learn from
- adaptation
The process of adjusting to new or changing conditions
- agglomerative clustering
A type of hierarchical clustering that groups similar data points together
- anomaly detection
The process of identifying data points that do not conform to expected patterns or behaviors
- area under the PR curve
A measure of a model's performance in classification tasks
- area under the ROC curve
A measure of a model's ability to distinguish between positive and negative classes