Kumo.ai home pagelogo
  • Community
  • Contact Support
  • Start for free
  • Start for free
Guide
Examples
Reference
FAQ
Releases
Get Started
  • Overview
  • Quick Start
Connect Data
  • Data Connectors
  • Connector Options
  • Select Tables
  • Column Preprocessing
  • Create Graph
Train Model
  • Predictive Query
  • Model Training
  • Model Settings
  • Evaluation
  • Explainability
Run Models
  • Batch Predictions
  • Outputs
  • Model Risk Management
Admin & Setup
  • Quotas and Limits
  • Data Processing Addendum
  • Browser
  • Deployment Options
  • SSO Configuration Guide
On this page
  • Column Processing
  • Model Architecture
  • Neighbor Sampling
  • Optimization
  • Training Job Plan
  • Training Table Generation

Model Planner Options

Fine-grained control over encoders, training strategy, and the AutoML search space.

Suggest Edits

​
Column Processing

  • encoder_overrides

​
Model Architecture

  • activation
  • aggregation
  • channels
  • handle_new_target_entities
  • module
  • normalization
  • num_post_message_passing_layers
  • num_pre_message_passing_layers
  • ranking_embedding_loss_coeff
  • output_embedding_dim
  • target_embedding_mode
  • use_seq_id
  • distance_measure

​
Neighbor Sampling

  • max_target_neighbors_per_entity
  • num_neighbors

​
Optimization

  • base_lr
  • batch_size
  • early_stopping
  • lr_scheduler
  • majority_sampling_ratio
  • max_epochs
  • max_steps_per_epoch
  • max_test_steps
  • max_val_steps
  • weight_decay
  • weight_mode

​
Training Job Plan

  • refit_full
  • refit_trainval
  • run_mode
  • metrics
  • num_experiments
  • tune_metric

​
Training Table Generation

  • entity_candidate_aggregation
  • forecast_length
  • forecast_type
  • lag_timesteps
  • year_over_year
  • split
  • timeframe_step
  • train_end_offset
  • train_start_offset
Powered by Mintlify
Assistant
Responses are generated using AI and may contain mistakes.