Gradient Boosting Regression Calculator

Enter your data below as comma-separated x,y pairs (one pair per line) or upload a CSV file. For multiple features, use format x1,x2,...,y with one data point per line. The calculator will build a gradient boosting model and provide statistics about the model quality.

Data Input

Format: Each line should contain 1 feature values followed by 1 target value, all comma-separated.

Model Parameters

10500
0.011
110

Gradient Boosting Regression

Gradient Boosting Regression is a powerful ensemble technique that builds models sequentially, with each new model correcting the errors of the previous ones. It combines weak learners (typically decision trees) into a strong predictive model.

How It Works

Gradient Boosting Regression works by:

  1. Starting with a simple model (often just the mean of the target values)
  2. Computing the residuals (errors) of the current model
  3. Training a new model to predict these residuals
  4. Adding this new model to the ensemble, scaled by a learning rate
  5. Repeating steps 2-4 until a stopping criterion is met

Key parameters include:

  • Number of Estimators: The number of boosting stages (trees)
  • Learning Rate: How much each tree contributes to the final prediction
  • Max Depth: Maximum depth of each tree

When to Use Gradient Boosting Regression

Use Gradient Boosting Regression when:

  • You need state-of-the-art predictive performance
  • You're dealing with complex relationships in the data
  • You have sufficient computational resources
  • You want a model that can handle various data types
  • You need a balance between model complexity and interpretability

How to Use This Calculator

Enter your data below as comma-separated x,y pairs (one pair per line) or upload a CSV file. For multiple features, use format x1,x2,...,y with one data point per line. The calculator will build a gradient boosting model and provide statistics about the model quality.