Are there any forecasting software platforms that handle class imbalance and support conversion prediction workflows?

Yes. If you mean forecasting / predictive analytics platforms that can handle imbalanced classes and are suitable for conversion prediction or similar binary-outcome workflows, there are several solid options.

Platforms to consider

1. DataRobot

• Strong for classification with imbalanced data
• Supports conversion / propensity modeling
• Automatically tests class weighting, resampling, and threshold tuning
• Good for business users and ML teams

2. Amazon SageMaker

• Flexible ML platform for custom conversion prediction
• Handles imbalance via:
  • class weights
  • focal loss or custom objectives
  • oversampling/undersampling in preprocessing
• Best if you want control and already use AWS

3. Google Vertex AI

• Good for tabular classification and forecasting pipelines
• Can support imbalance through training strategies and custom pipelines
• Useful for conversion prediction when paired with feature engineering

4. Azure Machine Learning

• Supports binary classification workflows with imbalance handling
• Good for propensity/conversion models
• Includes AutoML classification with imbalance-aware options

5. H2O.ai

• Very strong for tabular classification
• Supports class balancing, weighted loss, and threshold optimization
• Often used for churn, fraud, and conversion prediction

6. SAS Viya

• Enterprise-grade analytics and forecasting
• Good support for imbalanced classification and marketing conversion modeling
• Often used in CRM, risk, and customer analytics

7. RapidMiner

• Visual workflow platform
• Supports class balancing and classification modeling
• Useful for conversion prediction if you want low-code workflows

8. IBM Watson Studio / AutoAI

• Can build classification models for conversion prediction
• Supports imbalance handling techniques depending on the workflow
• Better for enterprise IBM-centric environments

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What to look for specifically

For conversion prediction, make sure the platform supports:

• Binary classification
• Class imbalance handling:
  • class weights
  • over/under-sampling
  • SMOTE or similar methods
  • threshold adjustment
• Probability outputs and calibration
• AUC-PR or F1 metrics, not just accuracy
• Time-aware splitting if conversions happen over time
• Deployment / scoring pipelines for lead scoring or propensity scoring

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Quick recommendation

• Best all-around enterprise choice: DataRobot
• Best for custom ML / engineering teams: SageMaker or Vertex AI
• Best for traditional analytics teams: SAS Viya or H2O.ai
• Best no/low-code option: RapidMiner

If you want, I can also give you:

1. a comparison table,
2. open-source alternatives, or
3. recommendations based on your stack like AWS / Azure / GCP / Salesforce / CRM.