An overview of the Feature and Tabular Analysis toolset

The Feature and Tabular Analysis toolset contains tools for applying machine learning and deep learning algorithms to feature or tabular data.

The Train Using AutoML tool uses automated machine learning (AutoML) to train and fine-tune machine learning models given training data and available compute resources. The trained models can be used in the Predict Using AutoML for predicting both categorical variables (classification) and continuous variables (regression).

Training machine learning (ML) models has traditionally been a complex process that required specialized knowledge of different types of models and how their parameters (known as hyperparameters) can be fine-tuned to get the best results. This is an iterative process that requires several experiments before the most accurate model and its appropriate hyperparameters can be identified. The AutoML tools automate this process without code. While doing so, they provide visibility into the performance and hyperparameters of the trained models, as well as insight into which features have the highest impact on the model results.


Train Using AutoML

Trains a machine learning model by building training pipelines and automating much of the training process. This includes exploratory data analysis, feature selection, feature engineering, model selection, hyperparameter tuning, and model training. Its outputs include performance metrics of the best model on the training data, as well as the trained deep learning model package .dlpk that can be used as input for the Predict Using AutoML tool to predict on a new dataset.

Predict Using AutoML

Predicts continuous variables (regression) or categorical variables (classification) on unseen compatible datasets using a trained .dlpk model produced by the Train Using AutoML tool.

Tools in the Feature and Tabular Analysis toolset

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