====================== Component availability ====================== Use this catalogue to find an existing component and its configuration parameters. The linked API categories describe the shipped implementations. Registration makes a method discoverable; its ``suitable`` check still determines whether it can run on the task and data at its position. See :doc:`pipelines/guide` to select training components and :doc:`pipelines/study` to select analytical components. Add a method missing from the catalogue with :doc:`adaptability`. Availability at a glance ======================== .. list-table:: :header-rows: 1 :widths: 27 20 53 * - Capability - Status - Scope * - Training and analytical families below - Implemented - Included in IAML; individual methods require compatible data and prediction outputs. * - ``LightGBMClassifier`` and ``LightGBMRegressor`` - Backend dependent - Available when ``lightgbm`` imports successfully. The adapters are excluded by suitability checks otherwise. * - ``WeibullAFT`` - Backend dependent - Uses a compatible scikit-survival ``WeibullAFT`` implementation or falls back to ``lifelines.WeibullAFTFitter``. Requires one of these backends to be available. * - Advanced domain types, a constraints DSL and partial replacement of complete subpipelines - Unsupported - Outside the current recipe API. See :ref:`pipelines-ref-open` for the supported boundaries. ``iaml.steps`` exposes readable names for IAML's training adapters, including those above. The historical ``Act...`` imports refer to the same classes. Prepare data ============ .. list-table:: :header-rows: 1 :widths: 30 70 * - API category - What it provides * - :doc:`Initial data preparation ` - Standardize formats before modeling, such as trimming whitespace, parsing dates, converting numeric strings and recognizing missing-value codes. * - :doc:`Cleaning and encoding ` - Handle missing values and convert categorical, textual or date columns into model inputs. Includes imputation, category encoders and text representations. * - :doc:`Feature selection ` - Retain informative variables and reduce redundant features, using variance, correlation, statistical tests or model-based selection. * - :doc:`Scaling and normalization ` - Adjust numerical feature scales for models sensitive to units or magnitudes. Includes standard, robust and min-max scaling. * - :doc:`Class balancing ` - Change the balance of classes in training data through over- or undersampling, including SMOTE. Resampling is skipped during prediction. * - :doc:`Feature transformations ` - Explore alternative representations through dimensionality reduction, projections, feature aggregation and distribution transformations. Includes PCA and quantile transformations. Predict outcomes ================ .. list-table:: :header-rows: 1 :widths: 30 70 * - API category - What it provides * - :doc:`Classification ` - Predict categorical outcomes, such as disease status or a clinical event. Individual estimators determine which target types and probability outputs they support. * - :doc:`Regression ` - Predict numerical outcomes, such as a measurement or length of stay, using linear models, trees, boosting and other estimator families. * - :doc:`Survival analysis ` - Model time-to-event outcomes with censoring. Predictors include Cox models, survival forests and survival boosting. Evaluate and optimize ===================== .. list-table:: :header-rows: 1 :widths: 30 70 * - API category - What it provides * - :doc:`Metrics ` - Score predictions for classification, regression or survival. The main metric defines the objective used to rank candidate pipelines. * - :doc:`Validation splitters ` - Define training and validation partitions for candidate evaluation, using cross-validation or holdout splits. Group information supports separation of patients or sites where required by the study. * - :doc:`Search optimizers ` - Propose further candidates from previous evaluations. Genetic search can change steps and their parameters, while Bayesian and random optimizers tune parameters within existing pipeline structures. Inspect data and results ======================== .. list-table:: :header-rows: 1 :widths: 30 70 * - API category - What it provides * - :doc:`Descriptive statistics ` - Summarize the dataset through missingness, distributions, variability and associations. These summaries describe the data rather than prediction performance. * - :doc:`Plots ` - Visualize data, model performance and SHAP contributions. Individual plot classes specify the task, inputs and prediction outputs they need. * - :doc:`Explanation adapters ` - Request ``KernelSHAP`` through a configured explanation collection. Calculations run on demand on a fitted classification or regression pipeline. See :doc:`architecture` for how these components work together.