Transformations Parameters
Solutions
| Transformation | Use Case | Equation / Calculation Method |
|---|---|---|
| Log Transformation | Stabilizing variance, normalizing data, useful for data with exponential changes (e.g., economic data). | y = log(x) |
| Square Root Transformation | Stabilizing variance, normalizing data, suitable for data sets with non-negative values (counts, areas). | y = √x |
| Box-Cox Transformation | Generalized form for stabilizing variance, making data more normal-like, applicable in various scenarios. | y(λ) = (x^λ -1)/λ, for x > 0 and λ ≠ 0 |
| Z-Score/Standard Score | Standardizing data to have mean of 0 and standard deviation of 1, used in outlier detection and normalization. | z = (x-µ)/σ |
| Min-Max Scaling | Scaling data to a fixed range (0-1), useful in scale-sensitive algorithms (neural networks, k-NN). | Xscaled = (X - Xmin) / (Xmax - Xmin) |
| Normalization (L1, L2 norms) | Scaling individual samples, essential for algorithms needing comparable scales (support vector machines). | |
| Difference Transformation | Making time-series data stationary by subtracting previous observation from the current one. | Δxt = xt - xt-1 |
| Categorical Encoding | Converting categorical data into a numeric format for use in mathematical models (One-Hot, Label Encoding). | One-Hot: Binary vectors, Label: Integer encoding |
| Binning/Discretization | Transforming continuous variables into discrete bins, simplifying complex relationships in data. | Interval: Equal-Width Quantile: Equal Frequency Count Tree-Based: Decision Tree |