Starlight Eval
Starlight Eval is a lightweight evaluation library for machine learning models in the
Starlight ecosystem.
It provides essential metrics for evaluating classification models such as
accuracy,
precision,
recall,
F1-score, and
confusion matrices.
Designed to work seamlessly with
starlight-classifier,
starlight-vec, and
starlight-ml.
---
Features
* Classification accuracy
* Precision, recall, and F1-score (per label)
* Confusion matrix generation
* Full classification report
* Zero dependencies
* Framework-agnostic
---
Installation
``
bash
npm install starlight-eval
`
---
š Importing
$3
`
js
import * as evalml from "starlight-eval";
`
$3
`
sl
import * as evalml from "starlight-eval";
`
---
Basic Usage
`
js
const yTrue = ["tech", "tech", "programming", "programming"];
const yPred = ["tech", "programming", "programming", "programming"];
console.log(evalml.accuracy(yTrue, yPred));
console.log(evalml.confusionMatrix(yTrue, yPred));
`
---
Classification Report
`
js
const report = evalml.classificationReport(yTrue, yPred);
console.log(report);
`
Example output:
`
js
{
tech: { precision: 0.5, recall: 0.5, f1: 0.5 },
programming: { precision: 0.67, recall: 1.0, f1: 0.8 },
accuracy: 0.75
}
``
---
Available Functions
$3
Returns the overall classification accuracy.
---
$3
Returns a label-to-label confusion matrix.
---
$3
Calculates precision for a given class label.
---
$3
Calculates recall for a given class label.
---
$3
Calculates F1-score for a given class label.
---
$3
Generates precision, recall, F1-score per label, plus overall accuracy.
---
Works Great With
*
starlight-ml ā Tokenization & text processing
*
starlight-vec ā TF-IDF vectorization
*
starlight-classifier ā Document classification
*
starlight-cluster ā Unsupervised learning
---
Philosophy
Starlight Eval is built to be:
* Simple
* Transparent
* Educational
* Production-ready
Perfect for learning ML concepts or building real applications.
---
License
MIT License
Ā© Dominex Macedon