This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
genpark-linear-regression-gradient-descent-engine-skill delivers zero-dependency, mathematically rigorous statistical modeling, time-series forecasting, and anomaly detection primitives engineered strictly using Python 3.9+ standard library.
Zero External Dependencies: Operates exclusively via pure Python (math, random, statistics, json). Zero pip install overhead, zero numpy/scipy compilation failures.
Enterprise Statistical Invariants: Implements formal Holt linear smoothing, modified Z-score & Tukey IQR anomaly bounds, Welch's t-test p-value estimations, multivariate linear regression via gradient descent, and Power Iteration PCA dimensionality reduction.
Native Anthropic MCP Protocol: Compliant with standard JSON-RPC 2.0 stdio MCP specifications for Claude Desktop, Cursor, and Windsurf.
Related MCP server: genpark-time-series-exponential-smoothing-forecaster-skill
flowchart TD
DataStream["Time-Series & Multidimensional Numerical Stream"] --> AnomalyDetector["Statistical Z-Score & IQR Anomaly Detector"]
AnomalyDetector -->|Outlier Detected| FlagAnomaly["Flag Outlier & Alert Telemetry"]
AnomalyDetector -->|Clean Stream| ModelingBranch{"Analytical Objective"}
ModelingBranch -->|Trend Forecasting| HoltForecaster["Holt Linear Exponential Smoothing Forecaster"]
ModelingBranch -->|Supervised Fitting| LinearRegression["Multivariate Gradient Descent Regression Engine"]
ModelingBranch -->|Hypothesis Testing| WelchTest["Welch's Two-Sample T-Test Evaluator"]
ModelingBranch -->|Dimensionality Reduction| PCAReducer["Power Iteration SVD/PCA Dimension Reducer"]
HoltForecaster --> Synthesis["Agent Synthesis & Statistical Report"]
LinearRegression --> Synthesis
WelchTest --> Synthesis
PCAReducer --> Synthesisfrom client import LinearRegressionGradientDescentEngine
# Initialize engine
engine = LinearRegressionGradientDescentEngine()
# Execute self-testing benchmark suite
result = engine.run_benchmark_linear_regression()
print("Execution Result:", result)Add to your claude_desktop_config.json or cursor.json:
{
"mcpServers": {
"genpark-linear-regression-gradient-descent-engine-skill": {
"command": "python",
"args": ["-u", "/path/to/genpark-linear-regression-gradient-descent-engine-skill/mcp_server.py"]
}
}
}This skill contains pre-configured smithery.yaml and pyproject.toml manifests. Install directly via pip:
pip install git+https://github.com/alphaparkinc/genpark-linear-regression-gradient-descent-engine-skill.gitIndependent statistical feature evaluation for machine learning and forecasting. ML Referee tests up to 1,000 candidate features in one run against a declared baseline, helping researchers and AI agents decide which inputs to pursue, which bounded feature repairs merit further testing, and where more evidence is needed—without a separate ablation run for every feature. Use the MCP tools to discover input schemas, check access and credits, preflight a dataset, obtain a quote, submit an authorized evaluation, and retrieve structured results and reports. Results are scoped to the submitted evidence and evaluation design; they do not guarantee future model performance. Invite-only beta with manual approval. Public tool discovery requires no authentication; account and evaluation tools require OAuth through approved client connections and available evaluation credits. Connect directly to https://api.mlreferee.com/mcp. Website: https://mlreferee.com Request beta access: https://mlreferee.com/beta Agent setup and documentation: https://mlreferee.com/agents
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