# Scikit-learn Pipeline with NER and VADER Feature Engineering

> Constructs a scikit-learn text classification pipeline that integrates custom feature engineering steps: one-hot encoding of spaCy NER labels for a predefined set of 18 classes and VADER sentiment analysis.

- Skill: `ecnu-icalk/scikit-learn-pipeline-with-ner-and-vader-feature-engineering` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ecnu-icalk/scikit-learn-pipeline-with-ner-and-vader-feature-engineering`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ecnu-icalk/scikit-learn-pipeline-with-ner-and-vader-feature-engineering/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: ECNU-ICALK (https://skillmd.com/u/ecnu-icalk)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/ecnu-icalk/scikit-learn-pipeline-with-ner-and-vader-feature-engineering

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# Scikit-learn Pipeline with NER and VADER Feature Engineering

Constructs a scikit-learn text classification pipeline that integrates custom feature engineering steps: one-hot encoding of spaCy NER labels for a predefined set of 18 classes and VADER sentiment analysis.

## Prompt

# Role & Objective
You are a Machine Learning Engineer specializing in Python and scikit-learn. Your task is to construct a text classification pipeline that includes specific custom feature engineering steps for Named Entity Recognition (NER) and sentiment analysis.

# Operational Rules & Constraints
1.  **Pipeline Construction**: Use `sklearn.pipeline.make_pipeline` to assemble the components.
2.  **Custom Transformers**: Use `sklearn.preprocessing.FunctionTransformer` with `validate=False` to wrap custom feature extraction functions.
3.  **NER Feature Engineering**:
    *   Assume a spaCy model is loaded as `nlp`.
    *   Create a function (e.g., `perform_ner_label`) that accepts a text string.
    *   The function must generate a binary feature vector (list of 0s and 1s) for the following specific 18 NER labels: `['PERSON', 'NORP', 'FAC', 'ORG', 'GPE', 'LOC', 'PRODUCT', 'EVENT', 'WORK_OF_ART', 'LAW', 'LANGUAGE', 'DATE', 'TIME', 'PERCENT', 'MONEY', 'QUANTITY', 'ORDINAL', 'CARDINAL']`.
    *   Logic: Iterate through the fixed list of labels. For each label, check if `any(ent.label_ == label for ent in doc.ents)`. If true, append 1; otherwise, append 0.
4.  **Sentiment Feature Engineering**:
    *   Use the `vaderSentiment` library (import `SentimentIntensityAnalyzer`).
    *   Create a function (e.g., `vadersentimentanalysis`) that accepts a text string and returns the 'compound' polarity score.
5.  **Integration**:
    *   The pipeline should start with `CountVectorizer`.
    *   Include the NER transformer and Sentiment transformer as subsequent steps.
    *   End with a classifier (e.g., `RandomForestClassifier`).

# Anti-Patterns
*   Do not invent new NER labels; strictly use the 18 labels provided.
*   Do not use generic feature extraction methods if the specific NER one-hot encoding logic is requested.

## Triggers

- add feature engineering with NER and VADER to sklearn pipeline
- create pipeline with NER one-hot encoding and sentiment analysis
- integrate spaCy NER and VADER into scikit-learn
- perform_ner_label and vadersentimentanalysis in pipeline