🔬 Research & Academia · Flowchart
Machine Learning Experiment Pipeline
The experiment pipeline figure for a machine-learning paper: data, preprocessing, splits, model training, evaluation, ablation and statistical testing.
Drawing diagram…
What this diagram shows
- Train, validation and test splits made explicit
- Hyper-parameter search kept inside the training loop
- Ablation and significance testing at the end
Prompt used
Machine learning experiment pipeline for a paper: raw dataset is cleaned and preprocessed, split into train, validation and test sets, features are engineered, models are trained with a hyper-parameter search using the validation set, the best model is evaluated once on the test set, ablation studies check each component, and results are compared with baselines using statistical significance tests over several random seeds.
Mermaid code
flowchart LR
D[("Raw dataset")] --> P["Clean and preprocess"]
P --> S{"Split"}
S --> TR["Train set"]
S --> VA["Validation set"]
S --> TE["Test set"]
TR --> FE["Feature engineering"]
FE --> MT["Train models"]
VA --> HS["Hyper-parameter search"]
MT --> HS
HS --> BM["Best model"]
BM --> EV["Evaluate once on test set"]
TE --> EV
EV --> AB["Ablation studies"]
EV --> BL["Compare with baselines<br/>several random seeds"]
BL --> ST["Significance tests"]
AB --> RS["Results and tables"]
ST --> RSRelated templates
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CONSORT Participant Flow Diagram
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Randomised Controlled Trial Design
Study-design figure for a randomised controlled trial: population, randomisation, two arms, measurements and outcomes.
Cohort Study Design
Design figure for a prospective cohort study: exposed and unexposed groups followed over time to compare outcomes.
Conceptual Framework (Research Model)
A conceptual framework with independent, mediating, moderating and dependent variables, plus control variables. Rename the boxes for your own study.
Qualitative Thematic Analysis Process
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