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@@ -133,8 +133,9 @@ The following variables may be set to alter the behaviour of dfatool components. | `DFATOOL_LMT_MIN_SAMPLES_LEAF` | 0.0 .. **0.1** .. 1.0, 3 .. *n* | Minimum samples that each leaf of a split candidate must contain. A value below 1.0 specifies a ratio of the total number of training samples. A value above 1 specifies an absolute number of samples. | | `DFATOOL_LMT_MAX_BINS` | 10 .. **120** | Number of bins used to determine optimal split. LMT default: 25. | | `DFATOOL_LMT_CRITERION` | **mse**, rmse, mae, poisson | Error metric to use when selecting best split. | -| `DFATOOL_ULS_ERROR_METRIC` | **ssr**, rmsd, mae, … | Error metric to use when selecting best-fitting function during unsupervised least squares (ULS) regression. Least squares regression itself minimzes root mean square deviation (rmsd), hence the equivalent (but partitioning-compatible) sum of squared residuals (ssr) is the default. Supports all metrics accepted by `--error-metric`. | +| `DFATOOL_ULS_ERROR_METRIC` | **ssr**, rmsd, **mae**, … | Error metric to use when selecting best-fitting function during unsupervised least squares (ULS) regression. By default, least squares regression minimzes root mean square deviation (rmsd), hence the equivalent (but partitioning-compatible) sum of squared residuals (ssr) is the default. If `DFATOOL_ULS_LOSS_FUNCTION` is set to another value than linear, the default is mean absolute error (mae). Supports all metrics accepted by `--error-metric`. | | `DFATOOL_ULS_FUNCTIONS` | a,b,… | List of function templates to use in ULS. Default: all supported functions. | +| `DFATOOL_ULS_LOSS_FUNCTION` | **linear**', soft\_l1, … | Loss function for least squares fitting, see `scipy.optimize.least_squares#loss` documentation. | | `DFATOOL_ULS_MIN_DISTINCT_VALUES` | 2 .. **3** .. *n* | Minimum number of unique values a parameter must take to be eligible for ULS | | `DFATOOL_ULS_SKIP_CODEPENDENT_CHECK` | **0**, 1 | Do not detect and remove co-dependent features in ULS. | | `DFATOOL_ULS_MIN_BOUND` | **-∞** .. *n* | Lower bound for ULS regression variables. Setting it to 0 can often be beneficial. | |