Discover the unique features that make our AutoML the state of the art. Find those that make your ML project advanced, super easy, and understandable.
Simplifies the entire machine learning process from data preprocessing to model deployment.
Efficiently pinpoints the most influential variables for optimal model performance.
Enables easy comparison and selection of models based on performance metrics.
The report from running AutoML will contain the table with information about each model score and the time needed to train the model.
MLJAR AutoML takes care of features preprocessing like missing values imputation and converting categoricals, it can also handle target values preprocessing.
All models in the AutoML are saved and loaded automatically. No need to call save() or load().
Optimizes model performance by automatically searching for the best combination of hyperparameters, saving time and effort.
Variety of algorithms
Choose from many algorithms such as: XGBoost, CatBoost, Neural Networks, Decision trees, Random forest and many more...