A sophisticated data processing skill for reducing multivariate complexity through Principal Component Analysis and varimax rotation.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install lake-warming-attribution-pca-decomposition
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install lake-warming-attribution-pca-decomposition using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is PCA Decomposition?
The PCA Decomposition skill provides a robust framework for reducing the dimensionality of multivariate datasets. By transforming many correlated variables into a smaller set of uncorrelated components, it allows developers and data scientists to streamline complex models without losing significant information. This Openclaw Skills utility is specifically optimized for interpretability, utilizing varimax rotation to maximize the variance of loadings and make the resulting factors easier to map back to real-world concepts.
Built on top of industry-standard libraries like scikit-learn and factor_analyzer, this skill is essential for exploratory data analysis (EDA) and preprocessing. Whether you are dealing with economic indicators, environmental sensors, or user behavior metrics, PCA Decomposition helps identify the hidden structures within your data while effectively mitigating issues like multicollinearity in downstream regression models.
PCA Decomposition Use Cases
- Reducing high-dimensional datasets into a manageable number of primary components.
- Identifying underlying latent factors or thematic groups within survey and sensor data.
- Eliminating multicollinearity between predictor variables before performing regression analysis.
- Conducting exploratory data analysis to find patterns in complex, multivariate environments.
How PCA Decomposition Works
- Data is ingested and automatically standardized using a scaler to ensure all variables contribute equally to the analysis.
- A global PCA is executed on the entire variable matrix rather than individual categories to capture total variance.
- Varimax rotation is applied to the factor loadings to clarify the relationship between original variables and components.
- Eigenvalues are calculated to determine the optimal number of factors based on the Kaiser Criterion.
- Factor scores are generated for each observation, providing a reduced-dimension dataset ready for attribution or predictive modeling.
PCA Decomposition Setup
To integrate this capability into your project using Openclaw Skills, ensure you have the necessary Python environment and dependencies installed:
pip install scikit-learn factor-analyzer pandas
Ensure your dataset is cleaned of missing values before initiating the decomposition workflow.
PCA Decomposition Data Schema & Taxonomy
The skill produces structured outputs that allow for both mathematical validation and conceptual interpretation:
| Component | Format | Description |
|---|---|---|
| Standardized Matrix | 2D Array | Original data scaled to zero mean and unit variance. |
| Factor Loadings | Matrix (n_vars x n_factors) | Correlation coefficients between original variables and components. |
| Component Scores | 2D Array | The transformed coordinates of the observations in the new factor space. |
| Eigenvalues | Vector | Variance explained by each principal component (used for factor selection). |
Loadings are generally interpreted as follows:
- > 0.7: Strong association
- 0.4 - 0.7: Moderate association
- < 0.4: Weak association
PCA Decomposition Advanced Features
- Support for Kaiser Criterion automation to dynamically select the number of factors based on variance thresholds.
- Global Attribution Analysis mode: Run one unified PCA across all categories to prevent information loss.
- Varimax Rotation integration for enhanced human-readability of abstract mathematical components.
- Domain Knowledge Overrides: Manually specify factor counts to align with known theoretical frameworks.
SKILL.md
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