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H2O Driverless AI Wizard

See how the Wizard helps you customize a machine learning model based on your unique data and use case.

Proactive Machine Learning Support

Automated machine learning (autoML) empowers data scientists to work faster and more efficiently by leveraging automation to accomplish key machine learning tasks that have previously required manual examination. AutoML is focused on creating a model with the highest accuracy, often focusing on smaller tasks like predicting a column in a dataset, detecting anomalies or identifying clusters in a dataset. It has gone from a niche tool to a critical business requirement, making autoML a market standard.

Task automation alone is no longer enough to help autoML users reach their larger objective of discovering solutions to complex, real-world problems. Automation should no longer be viewed simply as an alternative to manual data science tasks, but rather a complement to more holistic machine learning efforts. The H2O AI Wizard offers automated expertise with a built-in guidance system that proactively recommends data science best practices based on each unique dataset and use case.

Benefits

Streamline Best Practices

Proactive recommendations follow data science best practices across a variety of machine learning disciplines.

Improve Model Performance

Reduce risk and avoid common pitfalls with explicit advice on the best modeling techniques for your data and use case.

Expand Knowledge Base

Built-in educational components extend AI accessibility by explaining the importance of key data science concepts.

Streamline Best Practices

The H2O AI Wizard analyzes your dataset and helps you create an optimal dataset for modeling based on data science best practices. You can view recommendations and explanations of those best practices directly in the user interface.

For example, part of the machine learning process is to evaluate which columns in a dataset are useful in predicting some target. It would be tedious and time-consuming to manually go through and identify possible data leakage across all columns. With a pure autoML approach, there is a risk that columns would be dropped that should remain as a key feature in the dataset. The H2O AI Wizard brings the strongest aspects of human expertise and automation together to build highly accurate machine learning models.

H2O’s AI Wizard allows users to employ subject matter expertise to select and drop features with the potential to cause data leakage.

Improve Model Performance

Human input provides the H2O AI Wizard necessary context to create and improve model performance based on specific use case requirements.

Define how the model will be deployed.

Select your preferences for model complexity, deployment size, and training time.

The H2O AI Wizard recommends scoring functions based on the given use case.

Once models have been built, the H2O AI Wizard allows the user to assign dollar values to different model outcomes. How much gain occurs from a correct prediction? How much cost is associated with an incorrect prediction? This helps identify which model generates the greatest overall profit.

The H2O AI Wizard automatically alerts users to potential data issues, focusing on those that can specifically influence model accuracy. It reviews the target column providing distribution information and frequency of classes. This helps the user quickly identify that the target column is aligned with business expectations.

The H2O AI Wizard then automatically detects ID columns in the dataset. Including ID columns can negatively influence model performance by preventing it from generalizing on new datasets.

Signal strength detects how influential a column is in predicting a target. The H2O AI Wizard calculates models on demand to determine the signal strength of each feature. In the example below, the H2O AI Wizard found that the “Score” column has a signal strength of 100%. In this case, the “Score” column was completely correlated with the target, so the system recommends that it be dropped from modeling.

Signal strength detects how influential a column is in predicting a target. The H2O AI Wizard calculates models on demand to determine the signal strength of each feature. In the example below, the H2O AI Wizard found that the “Score” column has a signal strength of 100%. In this case, the “Score” column was completely correlated with the target, so the system recommends that it be dropped from modeling.

Expand Knowledge Base

The H2O AI Wizard instructs H2O Driverless AI on the appropriate machine learning techniques to select. For each question asked, an information panel opens to provide more details about each technique and its importance in model development. This educates users on data science and machine learning best practices.

While the built-in educational components of the H2O AI Wizard explains the importance of key data science concepts directly in the user interface, it also auto-generates the code needed to launch the same experiment from a Python notebook. This allows users to seamlessly switch between Python and the UI.

Start building models with the H2O AI Wizard with our 14-day Free Trial.