Quick Start

Train and evaluate your first model with IAML’s built-in pipeline. This walkthrough uses the defaults for preprocessing, model selection and cross-validation; no pipeline configuration is needed.

Install IAML

Use Python 3.10 or newer:

python -m pip install PyIAML

Your first study

Save this as example.py and run python example.py. The dataset is bundled with scikit-learn, so no data download is needed.

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from iaml import IAML

if __name__ == "__main__":
    X, y = load_breast_cancer(return_X_y=True, as_frame=True)
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, stratify=y, random_state=42
    )
    search = IAML(max_duration=30, max_workers=1)
    print(search.get_descriptive_statistics(X_train, y_train))
    search.fit(X_train, y_train)
    chosen_model = search.chosen_candidate
    print(chosen_model.evaluate(X_test, y_test))

The descriptive table is calculated before training. chosen_model is the best model found, including its preprocessing; evaluate returns its scores on the held-out test set. In this dataset, labels are 0 for malignant and 1 for benign.

The example uses one worker and a 30 second search budget. Final fitting can add time. Keep the __main__ guard when running a script, because training starts worker processes.

Continue the integrated workflow

See From data to explanations for a complete analysis with performance figures, SHAP explanations and an exportable record of the selected pipeline.

The guides follow the same study from training through reporting:

  • 01 / Build: set the task, validation design, search budget and fitting data.

  • 02 / Evaluate: assess held-out predictions and create performance figures.

  • 03 / Explain: inspect the selected pipeline and explain predictions with SHAP.

  • Study reporting: retain configurations, results and method references for your study report.

Descriptive statistics, performance plots and explanations are calculated when you request them; fitting does not run every available analysis.

Compose pipelines for your team

For reusable recipes and study-specific methods, continue with Customize IAML. This optional tour introduces the Pipeline API and component extensions while keeping the training and evaluation workflow shown here.