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Outliers—observations that deviate markedly from the bulk of data—can stem from measurement error, data entry mistakes, or genuine rare events. Statistical detection methods include Z-score analysis, which flags points beyond a threshold number of standard deviations from the mean, and the Interquartile Range (IQR) rule, which identifies values outside 1.5×IQR from the quartiles. Visualization tools such as boxplots and scatterplots provide intuitive means to spot anomalies. For multivariate data, distance-based metrics like the Mahalanobis distance assess how far an observation lies from the center of a multivariate distribution.
Treatment Approaches
Once detected, outliers can be addressed via removal, transformation, or capping. Trimming or winsorizing limits extreme values to specified percentiles, preserving sample size while reducing influence. Logarithmic or power transformations can mitigate skewness and compress extremes. In certain contexts, outliers represent valid rare phenomena and should be retained, albeit flagged for robust modeling techniques. Alternatively, domain knowledge may guide selective correction of data entry errors. The chosen strategy must align with analytical goals and the nature of the data to avoid discarding valuable information.
Note: This section includes information based on general knowledge, as specific supporting data was not available.
Data Preparation Techniques
Scaling and Normalization
Many machine learning algorithms—particularly those based on distance metrics—are sensitive to feature scale. Min-max scaling rescales values to a fixed range, typically [0,1], preserving relative distances but compressing outliers. Standardization transforms features to have zero mean and unit variance, making distributions comparable. Robust scaling uses median and IQR, offering resilience against extremes. Selection among these methods depends on algorithm requirements and the underlying data distribution. Proper scaling accelerates convergence in gradient-based models and prevents features with large magnitudes from dominating the objective function.
Encoding Categorical Data
Categorical variables require conversion to numerical representations. One-hot encoding creates binary indicator variables for each category, but can lead to high dimensionality in features with many levels. Label encoding assigns integer codes arbitrarily, which may introduce spurious ordinal relationships. Frequency or target encoding replaces categories with aggregate statistics, such as observed frequencies or mean target values, though care must be taken to avoid leakage in supervised settings. Advanced embedding techniques, often learned within neural architectures, can capture richer semantic relationships between categories while controlling dimensionality.
Note: This section includes information based on general knowledge, as specific supporting data was not available.