How does AutoML handle non-numeric data like lists or text?

It uses Natural Language Processing (NLP) with TF-IDF for text and 'multi-hot encoding' with a bag-of-words approach for list columns to ensure sparse data is correctly transformed for ML algorithms.

What is the primary purpose of the InterSystems AutoML reference?

The reference provides the low-level technical details required to understand how InterSystems IRIS automates the machine learning process, from feature engineering to model training and deployment.

Which algorithms are supported in this reference?

The skill covers XGBRegressor for regression tasks, and Neural Networks (TensorFlow/Keras), Logistic Regression, and Random Forest for classification tasks.

Does this skill help with data cleaning?

Yes, it details how AutoML replaces missing values using modes for categorical data and means for numeric data, as well as how it performs feature elimination to remove redundancy.

InterSystems AutoML Reference

Name: InterSystems AutoML Reference
Author: sorodriguezz

bysorodriguezz

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데이터 과학 및 ML

Provides specialized technical guidance for building and deploying predictive models using InterSystems IRIS AutoML.

This skill acts as a comprehensive technical reference for InterSystems IRIS AutoML, a component of the IntegratedML framework. It enables Claude to provide deep insights into the automated machine learning process, including advanced feature engineering like multi-hot encoding for lists and TF-IDF for text. Use this skill when you need to understand how InterSystems IRIS classifies data types, transforms columns, handles missing values, and implements specific algorithms like XGBoost, Neural Networks, and Logistic Regression to produce production-grade predictive models.

주요 기능

01Guidance on SQL-based deployment patterns within the InterSystems IRIS data platform

02Standardized data transformation including missing value imputation and quartiles binning

030 GitHub stars

04Automated feature engineering for text and list data using TF-IDF and Bag-of-words

05Detailed hyperparameters for XGBoost, TensorFlow/Keras Neural Networks, and Scikit-Learn models

06Sophisticated column classification for numeric, categorical, and temporal data

사용 사례

01Preparing InterSystems IRIS data schemas for optimal automated machine learning performance

02Troubleshooting model performance by understanding underlying algorithm configurations and data preprocessing

03Implementing custom feature engineering pipelines that align with IRIS AutoML's native transformations

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add sorodriguezz/skills-objectscript automl-reference

For use in Claude.ai and ChatGPT

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