Data Preprocessing Questions
The common techniques used for outlier detection in data preprocessing include:
1. Z-score method: This method calculates the standard deviation of a data point from the mean and identifies outliers based on a predefined threshold.
2. Modified Z-score method: Similar to the Z-score method, but it uses the median and median absolute deviation instead of the mean and standard deviation, making it more robust to outliers.
3. Box plot method: This method uses quartiles and interquartile range (IQR) to identify outliers. Data points outside a certain range (typically 1.5 times the IQR) are considered outliers.
4. Mahalanobis distance: This method measures the distance between a data point and the centroid of the data set, taking into account the covariance between variables. Points with a high Mahalanobis distance are considered outliers.
5. Density-based methods: These methods identify outliers based on the density of data points. Examples include DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and LOF (Local Outlier Factor).
6. Isolation Forest: This method constructs random decision trees to isolate outliers. The number of splits required to isolate a data point is used as a measure of its outlierness.
7. Support Vector Machines (SVM): SVM can be used for outlier detection by identifying data points that lie farthest from the separating hyperplane.
8. Robust statistical methods: These methods, such as robust regression or robust covariance estimation, are less sensitive to outliers and can be used to detect them.
It is important to note that the choice of outlier detection technique depends on the specific characteristics of the data and the problem at hand.