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Random Forest Regression

Ensemble of decision trees for robust regression on tabular data.

  • Model ID: random-forest-regression
  • Multi-output: Yes (predicts multiple target columns simultaneously)
  • Hardware: CPU (uses all available cores)
  • Specialist type: tabular

For classification use random-forest-classification. See the ML Classical index for the full model table.

When to Use​

  • Real estate valuation
  • Stock price prediction
  • Energy consumption forecasting
  • Customer lifetime value
  • Multi-output regression (predict multiple targets at once)

Quick Start​

Single-output​

from colabhive import ColabHive

client = ColabHive(api_key="your_api_key")

dataset = client.datasets.upload(
    name="my_dataset",
    file="./data.csv"
)

job = client.training.create(
    model="random-forest-regression",
    dataset_id=dataset.id,
    hyperparameters={
        "target_column": "price"
    }
)

job.wait()
print("Training completed!")
print(job.get_metrics())   # {"mae": ..., "rmse": ..., "r2": ...}

Multi-output​

job = client.training.create(
    model="random-forest-regression",
    dataset_id=dataset.id,
    hyperparameters={
        "target_columns": ["price", "demand", "rating"]
    }
)
Multi-output

Random Forest natively supports multi-output regression. A single forest predicts all targets simultaneously.

Try with Example Dataset​

Dataset Requirements​

Supports:

  • CSV (.csv)
  • JSONL (.jsonl)
  • Parquet (.parquet)

Your dataset should be tabular:

  • Features: numeric or categorical columns
  • Target: one column (single-output) or multiple columns (multi-output)
  • Minimum samples: 50 (recommended: 1000+)

Technical Details​

  • Framework: scikit-learn
  • Category: ml_classical
  • Hardware: CPU (uses all available cores)
  • Multi-output: Native support