Decision Tree Regression Calculator

Decision Tree Regression

Decision Tree Regression uses a tree-like model of decisions to predict continuous values. It splits the data into subsets based on feature values, creating a tree structure where each leaf node represents a prediction.

How It Works

Decision Tree Regression works by recursively partitioning the feature space into regions, where each region corresponds to a leaf node in the tree. The algorithm:

1. Selects the best feature and threshold to split the data
2. Splits the data into subsets based on this feature
3. Repeats the process recursively for each subset
4. Stops when a stopping criterion is met (e.g., maximum depth, minimum samples)

The prediction for a new data point is the average of the target values in the leaf node it falls into.

Key parameters include:

• Max Depth: Maximum depth of the tree
• Min Samples Split: Minimum number of samples required to split a node

When to Use Decision Tree Regression

Use Decision Tree Regression when:

• You need a model that's easy to interpret and explain
• Your data has non-linear relationships
• You want to capture complex interactions between features
• You don't need to perform extensive data preprocessing (trees handle missing values and categorical features well)

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 decision tree model and provide statistics about the model quality.