Random Forest 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 random forest 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

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Random Forest Regression

Random Forest Regression is an ensemble learning method that combines multiple decision trees to produce a more accurate and stable prediction. It reduces overfitting by averaging the predictions of many trees trained on different subsets of the data.

How It Works

Random Forest Regression works by:

  1. Creating multiple decision trees using bootstrap samples of the data
  2. At each node, considering only a random subset of features for splitting
  3. Growing each tree to its maximum size
  4. Averaging the predictions of all trees for the final prediction

Key parameters include:

  • Number of Estimators: The number of trees in the forest
  • Max Depth: Maximum depth of each tree
  • Min Samples Split: Minimum number of samples required to split a node

When to Use Random Forest Regression

Use Random Forest Regression when:

  • You want better performance than a single decision tree
  • You need a model that's robust to overfitting
  • You're dealing with high-dimensional data
  • You want to capture complex non-linear relationships
  • You need a measure of feature importance

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 random forest model and provide statistics about the model quality.