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Found 10 papers, 0 papers with code
A Vision for Semantically Enriched Data Science
The recent efforts in automation of machine learning or data science has achieved success in various tasks such as hyper-parameter optimization or model selection.
A Survey on Semantics in Automated Data Science
Data Scientists leverage common sense reasoning and domain knowledge to understand and enrich data for building predictive models.
How Much Automation Does a Data Scientist Want?
There is an active research thread in AI, \autoai, that aims to develop systems for automating end-to-end the DS/ML Lifecycle.
Semantic Annotation for Tabular Data
We propose $C^2$, a column to concept mapper that is based on a maximum likelihood estimation approach through ensembles.
How can AI Automate End-to-End Data Science?
Data science is labor-intensive and human experts are scarce but heavily involved in every aspect of it.
Automating Predictive Modeling Process using Reinforcement Learning
Building a good predictive model requires an array of activities such as data imputation, feature transformations, estimator selection, hyper-parameter search and ensemble construction.
Feature Engineering for Predictive Modeling using Reinforcement Learning
It involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling error for a given target.
Learning Feature Engineering for Classification
Feature engineering is the task of improving predictive modelling performance on a dataset by transforming its feature space.
Cognito: Automated Feature Engineering for Supervised Learning
In this paper, we present a novel system called "Cognito", that performs automatic feature engineering on a given dataset for supervised learning.
Automating Feature Engineering
Feature Engineering is the task of transforming the feature space in a given learning problem to improve the performance of a trained model.
