variance-explained-prediction-models
CommunitySelect and evaluate predictors for variance explained in prediction models.
Data & Analytics#statistical analysis#predictive modeling#overfitting#predictor selection#adjusted R-squared
Authormrl2013
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill helps users predict an outcome variable by selecting and evaluating predictors based on how much variance they explain, without overfitting.
Core Features & Use Cases
- Predictor Selection: Choose predictors that explain the most variance in the outcome variable.
- Model Evaluation: Use adjusted R-squared to evaluate model fit and avoid overfitting.
- Use Case: When building a predictive model for a new dataset, this Skill can help determine which variables to include for the best predictive performance.
Quick Start
Run the variance-explained-prediction-models skill to build a predictive model for your dataset.
Dependency Matrix
Required Modules
None requiredComponents
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: variance-explained-prediction-models Download link: https://github.com/mrl2013/p8483-and-p8400-assistant/archive/main.zip#variance-explained-prediction-models Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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