Building Models in Driverless AI

• Launching Driverless AI
  • Server startup files
  • Resources
  • User options
• Genetic Algorithm in Driverless AI
  • Understanding Genetic Algorithm
  • The Full Picture
  • Reading the Logs
  • Some Additional Details
• Before You Begin
  • Sampling in Driverless AI
  • Missing and Unseen Levels Handling
  • Imputation in Driverless AI
  • Reproducibility in Driverless AI
  • Driverless AI Transformations
  • Internal Validation Technique
  • Ensemble Learning in Driverless AI
  • Monotonicity Constraints
  • Data leakage and shift detection in Driverless AI
  • Variable importance in Driverless AI
  • Imbalanced modeling in Driverless AI
  • Wide Datasets in Driverless AI
  • GPUs in Driverless AI
  • Experiment Queuing In Driverless AI
  • Free up space on a DAI instance
  • Time Series Best Practices
  • Tips ‘n Tricks
  • Simple Configurations
• Experiments
  • Experiment Settings
  • Expert Settings
  • Scorers
  • New Experiments
  • Sharing Experiments
  • Experiment setup comparison
  • Completed Experiment Page
  • Model Insights
  • Model Scores
  • Experiment Graphs
  • Experiment Summary
  • Experiment performance
  • Model Performance on Another Dataset
  • Checkpointing, Rerunning, and Retraining Experiments
  • Model retraining using recipes
  • Leaderboards
  • Project Workspace
• Time Series in Driverless AI
  • Understanding Time Series
  • Rolling-Window-Based Predictions
  • Time Series Constraints
  • Time Series Use Case: Sales Forecasting
  • Using a Driverless AI Time Series Model to Forecast
  • Time Series Expert Settings
  • Time-series leaderboard mode
  • Additional Resources
• NLP in Driverless AI
  • NLP Feature Engineering and Modeling
  • NLP Feature Naming Convention
  • NLP Explainers
  • NLP Expert Settings
  • An NLP Example: Sentiment Analysis
  • NLP Models to Production
• Image Processing in Driverless AI
  • Supported File Types for Image processing
• Unsupervised Algorithms in Driverless AI (Experimental)
  • Isolation Forest Anomaly detection
  • KMeans Clustering
  • Truncated SVD (Dimensionality Reduction)
  • Unsupervised Custom Recipes
  • Expert control over Unsupervised Experiments