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1. Global Outliers (Point Outliers) Definition: A data point that deviates significantly from the rest of the dataset. Example: In a dataset of student ages (10-15 years), an entry with age 50 is a global outlier. 2. Contextual Outliers (Conditional Outliers) Definition: A data point that is only an outlier in a specific context. Example: A temperature of 30°C is normal in summer but an outlier in winter. 3. Collective Outliers Definition: A group of data points that, when considered together, show abnormal behavior, but individually they may not be outliers. Example: In a network traffic dataset, a sudden spike in traffic from multiple IPs at the same time could indicate a DDoS attack. 4. Natural Outliers Definition: Outliers that are naturally occurring and not errors. Example: In a sports dataset, Usain Bolt's 100m sprint time is much lower than others, but it's a natural outlier due to his exceptional performance. 5. Human-made Outliers (Error-based Outliers) Definition: Outliers caused by data entry errors or sensor malfunctions. Example: A patient's height recorded as 300 cm instead of 180 cm due to a typo. 1. Univariate Outliers Definition: Outliers detected by analyzing a single feature (variable) independently. Example: In a dataset of salaries, if most salaries range between $30,000 to $70,000 but one entry shows $500,000, this is a univariate outlier. Multivariate Outliers Definition: Outliers detected by analyzing relationships between multiple features (variables) simultaneously. Example: In a dataset with features "height" and "weight," a person with height = 200 cm and weight = 40 kg is a multivariate outlier since this combination is unusual despite individual values being normal.

Inside the book of statistics, explanations for all the types of outliers are the same except univariate outliers.

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I learned about Outliers: --> Outliers can: 1. divert the center, 2. cause Skewness, 3. Wrong insights, 4. Inefficient ML Model, 5. Wrong predictions. --> Other names for Outliers: Outliers, Deviants, Abnormalities, Anomalous points, Abberrvants observations. -->Types of Outliers: Uni-variate, Bi-variate, Multi-variate, Global, Point, Local, Contextual. --> How to handle Outliers: 1. Remove them 2. Transform them(log transform) 3. Impute with mean, median, mode 4. Seperate them and treat them separately 5. Use Robust Models

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jazakumullah Kharn ameen

In this lecture, I have learned to extract outliers.