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ModelWizard: Toward Interactive Model Construction

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Dylan Hutchison

Programming Languages · Submitted 15 Apr 2016

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim To future engineers designing data science languages and tools, we offer ModelWizard's design as a new model construction paradigm, speeding discovery of our universe's structure.

how it is stated · asserted 0.73 read the record →

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arXiv ID1604.04639 (version 1)
Pith IDVJ42PFRF, Pith's permanent record of this paper and its review
CitationDylan Hutchison. "ModelWizard: Toward Interactive Model Construction." arXiv:1604.04639 (2016). https://pith.science/paper/VJ42PFRF
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 6,213 input tokens.

What the abstract itself states, as probabilities: claims first 0.09 · states limitation 0.21 · human participants 0.07 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.15 · mentions code or data availability 0.06.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Data scientists engage in model construction to discover machine learning models that well explain a dataset, in terms of predictiveness, understandability and generalization across domains. Questions such as "what if we model common cause Z" and "what if Y's dependence on X reverses" inspire many candidate models to consider and compare, yet current tools emphasize constructing a final model all at once. To more naturally reflect exploration when debating numerous models, we propose an interactive model construction framework grounded in composable operations. Primitive operations capture core steps refining data and model that, when verified, form an inductive basis to prove model validity. Derived, composite operations enable advanced model families, both generic and specialized, abstracted away from low-level details. We prototype our envisioned framework in ModelWizard, a domain-specific language embedded in F# to construct Tabular models. We enumerate language design and demonstrate its use through several applications, emphasizing how language may facilitate creation of complex models. To future engineers designing data science languages and tools, we offer ModelWizard's design as a new model construction paradigm, speeding discovery of our universe's structure.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Data scientists engage in model construction to discover machine learning models that well explain a dataset, in terms of predictiveness, understandability and generalization across domains. background 1.00 0.05 0.03 asserted 1.00
s1 Questions such as "what if we model common cause Z" and "what if Y's dependence on X reverses" inspire many candidate models to consider and compare, yet current tools emphasize constructing a final model all at once. gap 0.83 0.06 0.04 asserted 0.98
s2 To more naturally reflect exploration when debating numerous models, we propose an interactive model construction framework grounded in composable operations. contribution 0.98 0.68 0.21 asserted 0.95
s3 Primitive operations capture core steps refining data and model that, when verified, form an inductive basis to prove model validity. method 0.91 0.48 0.08 asserted 1.00
s4 Derived, composite operations enable advanced model families, both generic and specialized, abstracted away from low-level details. method 0.83 0.62 0.06 asserted 1.00
s5 We prototype our envisioned framework in ModelWizard, a domain-specific language embedded in F# to construct Tabular models. contribution 0.63 0.95 0.06 asserted 1.00
s6 We enumerate language design and demonstrate its use through several applications, emphasizing how language may facilitate creation of complex models. contribution 0.81 0.92 0.11 asserted 0.98
s7 To future engineers designing data science languages and tools, we offer ModelWizard's design as a new model construction paradigm, speeding discovery of our universe's structure. future work 0.72 0.27 0.52 asserted 0.73

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from argument 0.68.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Data scientists engage in model construction to discover machine learning models that well explain a dataset, in terms of predictiveness, understandability and generalization across domains. Questions such as "what if we model common cause Z" and "what if Y's dependence on X reverses" inspire many candidate models to consider and compare, yet current tools emphasize constructing a final model all at once. To more naturally reflect exploration when debating numerous models, we propose an interactive model construction framework grounded in composable operations. Primitive operations capture core steps refining data and model that, when verified, form an inductive basis to prove model validity. Derived, composite operations enable advanced model families, both generic and specialized, abstracted away from low-level details. We prototype our envisioned framework in ModelWizard, a domain-specific language embedded in F# to construct Tabular models. We enumerate language design and demonstrate its use through several applications, emphasizing how language may facilitate creation of complex models. To future engineers designing data science languages and tools, we offer ModelWizard's design as a new model construction paradigm, speeding discovery of our universe's structure.

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What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites

Cites as background 2 papers
  • NoSQL and NewSQL databases [ref] are also in scope, though each additional database supported will require a separate set of drivers that operations interface with to access data— unless recent projects such as SQL++ [ref] , D4M [ref] , or the larger BigDawg proposal [ref] succeed in providing a common language interface to many types of databases.

    The SQL++ Query Language: Configurable, Unifying and Semi-structured · Addressing Big Data's Challenges · 0.90

  • See [ref] , [ref] and [ref] for different flavors of introduction to probabilistic programming.

    Probabilistic Programming Concepts · Probabilistic Programming · 1.00

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