tree
Classification and regression trees for R.
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
UnverifiedOverview
What is tree?
The tree package provides functions to create classification and regression trees in R. It is a powerful tool for predictive modeling, allowing users to build decision trees that can be used for both categorical and continuous outcomes.
Key differentiator
“The tree package stands out by offering a straightforward and efficient way to build classification and regression trees in R, making it an essential tool for predictive modeling tasks within the R ecosystem.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
The 'tree' package lacks some modern algorithms and techniques available in more recent packages like 'rpart' or 'randomForest'.
Performance degrades significantly with larger datasets, making it less suitable for big data applications.
The documentation lacks detailed examples and explanations of advanced usage scenarios, which can hinder new users.
Fit analysis
Who is it for?
✓ Best for
Researchers and analysts who need to build classification or regression trees for predictive analytics.
Educators teaching machine learning concepts, particularly decision trees in R.
✕ Not a fit for
Projects requiring real-time predictions as the package is designed for batch processing.
Large-scale data sets where computational efficiency is critical.
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Works well with
Integrations
Next step
Get Started with tree
Step-by-step setup guide with code examples and common gotchas.