SplitPilot

An explainable toolkit for designing and validating machine-learning evaluation splits

SplitPilot is a Python package for choosing, executing, and validating train/test splitting strategies based on the structural characteristics of a dataset.

Instead of treating train_test_split() as a universal solution, SplitPilot considers signals such as repeated entities, temporal information, and grouping structure to recommend a more appropriate evaluation strategy.

Project status: Early development / alpha
License: MIT
Python: 3.10+

---

Table of Contents

1. Project Overview
2. The Problem
3. Project Objective
4. Core Idea
5. How SplitPilot Works
6. Supported Split Strategies
7. Why Split Strategy Matters
8. Architecture
9. Project Structure
10. Installation
11. Quick Start
12. Recommendation Workflow
13. SplitResult
14. Validation and Leakage Checks
15. Testing
16. Design Decisions
17. Current Limitations
18. Roadmap
19. Example Use Cases
20. Frequently Asked Questions
21. Interview Questions and Answers
22. Contributing
23. License

---

1. Project Overview

SplitPilot addresses an important but frequently overlooked part of machine-learning experimentation:

How should a dataset be divided so that model evaluation reflects the way the model will actually be used?

A random split is convenient, but convenience does not guarantee a valid evaluation.

For example, if a dataset contains multiple observations for the same customer, randomly distributing rows can place the same customer in both training and testing data. The model may then benefit from information about an entity during training that also appears in the test set.

Likewise, if the data represents events over time, randomly mixing older and newer observations can allow future information to influence evaluation of the past.

SplitPilot therefore treats dataset splitting as an evaluation-design problem, not merely a preprocessing operation.

---

2. The Problem

A conventional workflow often looks like this:

Raw Dataset
    |
    v
train_test_split()
    |
    v
Train Model
    |
    v
Evaluate Model

The problem is that the default random split may not respect the structure of the data.

Consider a customer transaction dataset:

Customer	Date	Transaction	Churn
A	Jan 01	100	0
A	Jan 15	150	0
A	Feb 20	120	1
B	Jan 03	80	0
B	Mar 02	210	1

A random row-level split could place observations from customer A into both training and test sets.

That produces an evaluation question that may not match the intended real-world question.

Instead of:

"Can the model generalize to unseen customers or future observations?"

the experiment may effectively ask:

"Can the model predict another observation from an entity it has already seen?"

These are different evaluation problems.

---

3. Project Objective

The primary objective of SplitPilot is to make dataset splitting more deliberate and explainable.

The package is designed around three principles:

3.1 Inspect dataset structure

Identify useful structural signals such as:

• repeated entity identifiers
• temporal columns
• potential grouping columns
• target structure

3.2 Recommend an evaluation strategy

Use those signals to recommend an appropriate splitting approach.

3.3 Execute and validate the split

Produce train/test datasets while enforcing the selected structural constraints.

The intended workflow is:

flowchart TD
    A[Dataset] --> B[Inspect Structure]
    B --> C[Detect Repeated Entities]
    B --> D[Detect Temporal Information]
    C --> E[Recommendation]
    D --> E
    E --> F[Select Split Strategy]
    F --> G[Execute Split]
    G --> H[Validate Evaluation Boundaries]
    H --> I[Train and Evaluate Model]

---

4. Core Idea

SplitPilot separates two related but different responsibilities:

Responsibility	Purpose
Recommendation	Decide which splitting strategy is appropriate
Splitting	Actually construct train/test datasets
Validation	Check whether important boundaries were respected
Explanation	Tell the user why a strategy was recommended

This distinction is important because a splitting library should not simply return arrays. It should help the user understand why the split exists in its particular form.

---

5. How SplitPilot Works

At a high level:

flowchart LR
    A[DataFrame] --> B[Profiler]
    B --> C[Recommender]
    C --> D[Recommendation]
    D --> E[DatasetSplitter]
    E --> F[SplitResult]
    F --> G[Validation]

The current package is organized around several components:

Component	Role
Profiler	Examines dataset characteristics
Recommender	Produces a split recommendation
DatasetSplitter	Executes the requested split
SplitResult	Provides a structured result
Recommendation model	Represents recommendation information
Pilot	Provides the higher-level user-facing workflow

The exact implementation is intentionally modular so that recommendation logic and splitting logic can evolve independently.

---

6. Supported Split Strategies

SplitPilot currently supports the following strategies.

6.1 Random Split

The conventional row-level train/test split.

Rows
 |
 +-- Training
 |
 +-- Testing

Conceptually:

train_test_split(
    X,
    y,
    test_size=0.2,
    random_state=42
)

Appropriate when

• observations can reasonably be treated as independent
• there is no important entity boundary
• there is no meaningful temporal ordering
• random sampling represents the intended deployment scenario

Risk

If repeated entities or temporal dependencies exist, a random split can create leakage or an overly optimistic evaluation.

---

6.2 Group Split

Group splitting keeps all observations belonging to the same group on one side of the split.

Example:

Customer A ───────────────> TRAIN
Customer B ───────────────> TRAIN
Customer C ───────────────> TEST
Customer D ───────────────> TEST

For customer-level data, the group column might be:

group_column="customer_id"

The important property is:

TRAIN groups ∩ TEST groups = ∅

Appropriate when

• observations belong to entities
• the model should generalize to unseen entities
• repeated observations exist for the same entity

---

6.3 Group-Stratified Split

Group-stratified splitting attempts to maintain a target-related distribution while still separating groups.

The implementation creates a group-level target summary and uses that summary to form strata before splitting groups.

Conceptually:

Individual observations
        |
        v
Aggregate by group
        |
        v
Group-level target distribution
        |
        v
Stratified group split
        |
        +-------- TRAIN GROUPS
        |
        +-------- TEST GROUPS

Appropriate when

• groups must remain isolated
• target distribution matters
• the dataset contains enough groups to support stratification

Important consideration

The current implementation uses a median-based binary stratum derived from the group-level target mean. This is a practical initial approach, not a universal stratification algorithm.

---

6.4 Time Split

Time splitting respects chronological ordering.

PAST ------------------------------------> FUTURE

|---------------- TRAIN -----------------|--- TEST ---|

The implementation:

1. converts the specified time column to datetime
2. rejects invalid dates
3. sorts observations chronologically
4. creates the train/test boundary according to test_size

Example:

strategy="time"
time_column="transaction_date"

Appropriate when

• future predictions are the real deployment scenario
• historical data is used to predict later observations
• temporal ordering contains meaningful information

Core principle

The test set should represent a later period than the training set.

---

6.5 Group-Time Split

Group-time splitting combines entity separation with chronological ordering.

This strategy is useful when both conditions matter:

1. groups should not appear in both train and test
2. the split should respect time

Conceptually:

                         TIME
              -------------------------------->

TRAIN GROUPS     |-----------------------------|

TEST GROUPS                                   |--------|
                 ^                             ^
                 |                             |
            train boundary               future test period

The current implementation first orders the data by the supplied time column, derives the ordered group sequence, and assigns groups to train/test according to the requested test proportion.

This strategy was specifically tested for:

• no group overlap
• chronological ordering
• leakage prevention

---

7. Why Split Strategy Matters

A model score is only meaningful relative to the evaluation design that produced it.

Suppose two experiments report:

Experiment	Split	Accuracy
A	Random	94%
B	Group	82%

It would be incorrect to immediately conclude that experiment A produced a better model.

The two experiments may be answering different questions.

A useful mental model is:

Model Performance
       |
       v
Evaluation Protocol
       |
       v
Split Strategy
       |
       v
Assumptions About Real-World Data

Therefore, SplitPilot focuses on making the evaluation protocol explicit.

---

8. Architecture

The current source architecture follows a src layout:

splitpilot/
|
+-- src/
|   |
|   +-- splitpilot/
|       |
|       +-- __init__.py
|       |
|       +-- core/
|       |   +-- __init__.py
|       |   +-- pilot.py
|       |   +-- profiler.py
|       |   +-- recommender.py
|       |   +-- splitter.py
|       |
|       +-- models/
|           +-- __init__.py
|           +-- recommendation.py
|
+-- tests/
|   +-- test_splitter.py
|
+-- CHANGELOG.md
+-- LICENSE
+-- README.md
+-- pyproject.toml

The src/splitpilot directory is the actual Python package. The outer splitpilot directory is the project/repository root.

This structure helps keep package source code separate from project-level files.

---

9. Project Structure

Path	Purpose
src/splitpilot/core/pilot.py	High-level package workflow
src/splitpilot/core/profiler.py	Dataset structure analysis
src/splitpilot/core/recommender.py	Strategy recommendation
src/splitpilot/core/splitter.py	Train/test split execution
src/splitpilot/models/recommendation.py	Recommendation data model
tests/test_splitter.py	Splitter test suite
pyproject.toml	Package metadata and build configuration
README.md	Project documentation
CHANGELOG.md	Version/change history
LICENSE	MIT license

---

10. Installation

From the source repository

Clone the repository and install the package in editable mode:

git clone https://github.com/krishgupta129/splitpilot.git
cd splitpilot
pip install -e .

For development dependencies:

pip install -e ".[dev]"

The development extra currently includes pytest.

Dependencies

The package currently depends on:

• Python 3.10+
• pandas
• scikit-learn

---

11. Quick Start

A high-level workflow can be used through Pilot.

import pandas as pd
from splitpilot import Pilot

df = pd.read_excel("customer_churn.xlsx")

pilot = Pilot(
    df,
    target="churn"
)

recommendation = pilot.recommend()

print(recommendation)

result = pilot.split()

print(result.X_train.shape)
print(result.X_test.shape)
print(result.strategy)

A recommendation may identify a strategy such as:

SplitRecommendation(
    strategy='group_time',
    ...
    group_column='customer_id',
    time_column='transaction_date'
)

The exact recommendation depends on the structure of the supplied dataset.

---

12. Recommendation Workflow

The recommended workflow is:

sequenceDiagram
    participant U as User
    participant P as Pilot
    participant F as Profiler
    participant R as Recommender
    participant S as Splitter

    U->>P: Provide DataFrame and target
    P->>F: Inspect dataset
    F-->>P: Structural signals
    P->>R: Evaluate signals
    R-->>P: SplitRecommendation
    U->>P: Request split
    P->>S: Execute recommended strategy
    S-->>P: SplitResult
    P-->>U: Train/test data

This workflow separates decision-making from execution.

That makes the package easier to test and makes recommendations easier to explain during experimentation.

---

13. SplitResult

The splitter returns a SplitResult dataclass containing:

Attribute	Type	Meaning
X_train	pd.DataFrame	Training features
X_test	pd.DataFrame	Testing features
y_train	pd.Series	Training target
y_test	pd.Series	Testing target
strategy	str	Strategy used

Example:

result = pilot.split()

print(result.X_train)
print(result.X_test)
print(result.y_train)
print(result.y_test)
print(result.strategy)

The result also supports tuple-style unpacking:

X_train, X_test, y_train, y_test = result

This provides a familiar interface while retaining metadata through the structured result object.

---

14. Validation and Leakage Checks

A useful split should be validated rather than trusted simply because the function executed successfully.

For grouped data, an important validation is:

train_ids = set(result.X_train["customer_id"])
test_ids = set(result.X_test["customer_id"])

print(train_ids.intersection(test_ids))

Expected result:

set()

This verifies that no customer appears in both partitions.

For temporal splits, inspect the date ranges:

print(result.X_train["transaction_date"].min())
print(result.X_train["transaction_date"].max())

print(result.X_test["transaction_date"].min())
print(result.X_test["transaction_date"].max())

The expected relationship is:

max(train_time) <= min(test_time)

when the evaluation design requires a strictly chronological boundary.

Validation philosophy

SplitPilot treats successful execution and valid evaluation as related but distinct concepts:

Function runs successfully
          |
          v
Correct shapes?
          |
          v
Correct group boundaries?
          |
          v
Correct temporal ordering?
          |
          v
Acceptable evaluation design

---

15. Testing

The splitter has a dedicated pytest test suite.

The current test run reports:

12 passed in 1.15s

The tested areas include:

Test	Purpose
Random split shapes	Checks expected train/test dimensions
Group split overlap	Verifies group separation
Group-stratified split overlap	Verifies group separation
Time split chronology	Verifies temporal ordering
Group-time split overlap	Verifies group separation
Group-time chronology	Verifies temporal ordering
Group-time leakage prevention	Verifies structural isolation
Missing target	Validates error handling
Invalid test size	Validates parameter checking
Missing group column	Validates group configuration
Missing time column	Validates temporal configuration
Unknown strategy	Validates unsupported strategy handling

Run the full suite with:

pytest tests/test_splitter.py -v

A successful run should report all tests as passed.

---

16. Design Decisions

16.1 Why use a recommendation layer?

A user should not need to manually remember every condition under which a random split becomes questionable.

The recommendation layer converts structural observations into an explicit suggestion.

This also makes the reasoning inspectable:

Dataset
  |
  +-- repeated customer_id
  |
  +-- transaction_date detected
  |
  v
Recommendation:
group_time

---

16.2 Why return a dataclass?

Returning a raw tuple is familiar, but it loses useful metadata.

A SplitResult provides:

• named outputs
• explicit strategy information
• easier debugging
• clearer documentation
• compatibility with tuple-style unpacking

---

16.3 Why validate columns before splitting?

Failing early is preferable to allowing a cryptic downstream error.

For example:

if target not in df.columns:
    raise ValueError(...)

The same principle is applied to group and time columns.

---

16.4 Why validate temporal values?

A column may exist while containing invalid dates.

Therefore, the time-based implementation converts values with:

pd.to_datetime(..., errors="coerce")

and raises an explicit error if invalid values are detected.

This prevents silent corruption of the chronological split.

---

17. Current Limitations

SplitPilot is intentionally an early-stage project. Its current behavior should therefore be understood in that context.

17.1 Recommendation heuristics are not universal truth

A recommendation is a structured suggestion based on detected dataset characteristics. It does not replace domain knowledge.

17.2 Group-stratification is currently simplified

The current implementation creates a group-level target summary and uses a median-based binary stratum.

This is useful for the initial implementation, but more sophisticated target-distribution strategies may be appropriate for some datasets.

17.3 Group-time behavior depends on the data structure

Combining grouping and chronology is inherently more complicated than a conventional random split.

The implementation currently uses ordered groups derived after sorting by the supplied time column. Different real-world longitudinal structures may require more specialized strategies.

17.4 Small datasets can impose practical constraints

Strategies involving groups or stratification require enough groups and sufficiently varied target information to construct meaningful partitions.

17.5 Recommendation does not guarantee absence of every form of leakage

SplitPilot focuses on structural split boundaries. Feature engineering, preprocessing, target construction, duplicated information, and other parts of an ML pipeline can also introduce leakage.

The split is one part of a broader evaluation protocol.

---

18. Roadmap

Potential future development areas include:

Area	Possible improvement
Recommendation engine	More robust dataset-structure heuristics
Group stratification	More flexible target-distribution handling
Temporal splitting	More sophisticated longitudinal strategies
Validation	Dedicated automated leakage diagnostics
Diagnostics	Human-readable validation reports
API	More configurable splitting policies
Testing	Broader edge-case and property-based testing
Documentation	More real-world datasets and case studies
Packaging	Continued PyPI release improvements
CI	Automated testing across supported Python versions

The roadmap is intentionally open. Future features should be driven by real evaluation problems rather than adding complexity for its own sake.

---

19. Example Use Cases

19.1 Customer churn

Dataset structure

• multiple transactions per customer
• transaction timestamps
• customer-level prediction target

Potential recommendation:

group_time

because both customer boundaries and temporal ordering may matter.

---

19.2 Medical records

If several records belong to the same patient, a random row-level split may allow the same patient to appear in both partitions.

Potential strategy:

group

with:

group_column="patient_id"

The appropriate choice ultimately depends on the intended deployment scenario.

---

19.3 Financial forecasting

When historical observations are used to predict future outcomes:

time

may be more appropriate than a random split.

Example:

time_column="date"

---

19.4 Independent tabular observations

For genuinely independent observations where no meaningful entity or temporal boundary exists:

random

may be appropriate.

The key is not to avoid random splitting. The key is to avoid using it automatically when the dataset structure says otherwise.

---

20. Frequently Asked Questions

What is SplitPilot?

SplitPilot is an explainable Python toolkit for designing and validating machine-learning evaluation splits.

Its goal is to help users select a train/test strategy based on dataset structure rather than defaulting to a random split.

---

Is SplitPilot a machine-learning library?

No.

SplitPilot does not train models or replace libraries such as scikit-learn.

Its focus is the evaluation-split layer of the machine-learning workflow.

---

Does SplitPilot replace train_test_split()?

No.

Random splitting is still a valid strategy when the dataset assumptions support it.

SplitPilot provides additional strategies and a recommendation layer for situations where ordinary random sampling may not represent the intended evaluation scenario.

---

What problem does SplitPilot solve?

It addresses structural problems in train/test evaluation, particularly:

• repeated entities
• group overlap
• temporal ordering
• potential evaluation leakage caused by inappropriate partitioning

---

What is data leakage in this context?

Data leakage occurs when information that should not be available to the model during evaluation influences the training process.

A common structural example is placing observations from the same entity into both training and testing partitions when the intended task is generalization to unseen entities.

SplitPilot reduces this particular class of risk by enforcing appropriate group or temporal boundaries.

---

Why not always use random splitting?

Because rows are not always independent.

If ten rows represent ten measurements from one customer, randomly splitting those rows is different from splitting ten independent customers.

The correct split depends on the question the model is supposed to answer.

---

What is the difference between group and group-time splitting?

Group split

Separates entities:

TRAIN: Customer A, B, C
TEST:  Customer D, E

Group-time split

Attempts to respect both entity boundaries and temporal ordering:

TRAIN: earlier groups
TEST:  later groups

The latter is useful when both repeated entities and time are important to the evaluation design.

---

Does group splitting guarantee no leakage?

No.

It guarantees the specific group-boundary constraint implemented by the splitter.

Other forms of leakage can still come from:

• feature engineering
• preprocessing
• target-derived features
• duplicated records
• improperly constructed labels
• external information

Therefore, group isolation should be considered one leakage-control mechanism rather than a complete leakage detector.

---

Does a recommended strategy have to be followed?

No.

The recommendation is guidance.

A domain expert may deliberately choose another strategy if the actual deployment scenario calls for it.

The important part is that the decision should be intentional and explainable.

---

Why is explainability important for a data-splitting library?

Because evaluation methodology directly affects model metrics.

If a model achieves 95% accuracy, the next question should be:

"95% under what evaluation protocol?"

A recommendation that explains its reasoning makes that protocol easier to understand, reproduce, and discuss.

---

Why use a src directory?

The project uses the standard src layout:

project/
└── src/
    └── splitpilot/

The repository root contains project-level files, while src/splitpilot contains the actual Python package.

This creates a clear separation between source code and repository tooling.

---

What does SplitResult provide?

SplitResult stores:

X_train
X_test
y_train
y_test
strategy

It therefore provides both the split datasets and information about how the split was generated.

---

Can SplitResult still be unpacked like a tuple?

Yes.

The implementation provides __iter__, so this is supported:

X_train, X_test, y_train, y_test = result

---

How does SplitPilot handle invalid dates?

The time-based splitter converts the specified time column to datetime.

Invalid values are detected and result in a ValueError rather than silently producing an invalid chronological split.

---

Is SplitPilot production-ready?

Not yet.

The project is currently in early development / alpha.

The current implementation is suitable for development, experimentation, learning, and continued package engineering, but users should review its assumptions before relying on it for critical production evaluation pipelines.

---

21. Interview Questions and Answers

This section is intentionally written as an interview-preparation guide. The goal is to explain the project from both a software-engineering and machine-learning perspective.

---

Q1. Explain your project in one minute.

Answer:

SplitPilot is a Python toolkit I built to make machine-learning train/test splitting more deliberate and explainable.

The basic problem is that random splitting is not always appropriate. If a dataset contains repeated entities, such as multiple transactions from the same customer, random row-level splitting can place the same customer in both training and testing data. Similarly, time-dependent data should generally respect chronological ordering.

SplitPilot profiles the dataset, recommends a strategy such as random, group, group-stratified, time, or group-time splitting, and then executes that strategy while exposing the resulting train/test partitions through a structured SplitResult.

The main idea is that dataset splitting is part of evaluation design, not just a preprocessing command.

---

Q2. Why did you build SplitPilot?

Answer:

I wanted to address a practical machine-learning problem that is easy to overlook.

A model can have an impressive evaluation score while the evaluation protocol itself is unrealistic.

Instead of building another model-training wrapper, I focused on the data-splitting stage because the split determines what kind of generalization is actually being measured.

---

Q3. Why is train_test_split() sometimes insufficient?

Answer:

train_test_split() performs a random partition by default. That is appropriate when observations can reasonably be treated as independent and identically distributed for the intended evaluation.

However, many real datasets contain structure.

For example, multiple rows may belong to the same customer, patient, device, or account. Other datasets have a meaningful temporal order.

Randomly mixing those observations can produce an evaluation that does not represent the deployment scenario.

---

Q4. What is group leakage?

Answer:

Group leakage in this context occurs when the same entity appears in both training and testing partitions even though the intended evaluation requires unseen entities.

For example, if customer A has ten transactions and random splitting places six transactions in training and four in testing, the model has already seen customer A during training.

A group split prevents this by ensuring:

TRAIN groups ∩ TEST groups = ∅

---

Q5. What is temporal leakage?

Answer:

Temporal leakage occurs when information from the future influences an evaluation that is supposed to represent prediction of future observations.

For a forecasting-style problem, training should generally use historical information and testing should represent later observations.

A time split therefore establishes a chronological boundary rather than randomly mixing all observations.

---

Q6. Why did you implement both group and time strategies?

Answer:

They address different structural assumptions.

A group split answers an entity-generalization question:

Can the model generalize to groups it has not seen?

A time split answers a temporal-generalization question:

Can the model use historical data to predict later observations?

Some datasets require both constraints, which motivated the group-time strategy.

---

Q7. Why is group-time splitting difficult?

Answer:

Because two constraints must be satisfied simultaneously.

The split should respect group boundaries while also maintaining temporal ordering.

A naive implementation can accidentally solve one problem while violating the other.

That is why I added explicit tests for group overlap, chronology, and leakage prevention rather than checking only whether the function returned without an exception.

---

Q8. How did you validate your group-time implementation?

Answer:

I wrote tests covering three important properties:

1. No group appears in both train and test.
2. The split respects chronological ordering.
3. The resulting partition prevents the intended structural leakage.

The current splitter test suite contains 12 tests, and the latest run completed with all 12 passing.

---

Q9. Why return a dataclass instead of a tuple?

Answer:

A tuple is convenient, but it does not communicate what each value represents and does not provide room for metadata.

SplitResult gives named fields:

X_train
X_test
y_train
y_test
strategy

At the same time, I preserved tuple-style unpacking through __iter__.

So the API provides structure without sacrificing familiarity.

---

Q10. Why does SplitPilot have a profiler and recommender?

Answer:

I wanted to separate observation from decision.

The profiler identifies characteristics of the dataset, such as repeated entities and temporal information.

The recommender uses those signals to produce a strategy recommendation.

That separation makes the architecture easier to test and allows the recommendation logic to evolve independently from the actual splitting implementation.

---

Q11. Is the recommendation always correct?

Answer:

No.

It is a recommendation based on detectable structural signals, not a replacement for domain knowledge.

For example, detecting a customer identifier does not automatically tell us whether the real deployment scenario involves unseen customers.

The user still needs to understand the prediction task and choose an evaluation protocol accordingly.

---

Q12. Why did you use pandas and scikit-learn?

Answer:

Pandas is the natural interface for tabular dataset manipulation, and scikit-learn already provides reliable primitives for several splitting operations.

Instead of reimplementing every low-level operation, SplitPilot builds an explainable layer around those primitives and adds strategies that enforce the structural constraints required by the project.

---

Q13. How does the time splitter work?

Answer:

It first validates the time column, converts it to datetime, checks for invalid values, sorts the dataset chronologically, and then calculates a train/test boundary using the requested test size.

The important property is that the temporal ordering is established before the partition is created.

---

Q14. Why do you validate missing columns yourself?

Answer:

Explicit validation produces clearer errors.

For example, if the target column is missing, I raise:

Target column '...' was not found in the dataset.

This is more useful to the user than allowing a later pandas or scikit-learn operation to fail with a less contextual error.

---

Q15. What happens if an invalid strategy is provided?

Answer:

The splitter raises a ValueError identifying the unknown strategy.

This prevents silent fallback to another splitting method.

---

Q16. What was one challenge you encountered while developing the project?

Answer:

One important challenge was making group-time splitting satisfy both entity and temporal constraints.

An implementation can appear reasonable while still producing a misleading evaluation boundary.

I therefore treated the behavior as something to test explicitly rather than assuming that a successful function call meant the strategy was correct.

---

Q17. How would you improve SplitPilot?

Answer:

I would improve it in several stages.

First, I would strengthen the recommendation heuristics.

Second, I would make group-stratification more flexible because the current median-based binary grouping is intentionally simple.

Third, I would add stronger automated leakage diagnostics.

Finally, I would expand testing across more edge cases and Python versions and add continuous integration.

---

Q18. What would you do if a user disagrees with the recommendation?

Answer:

The recommendation should be treated as guidance rather than an enforced decision.

I would ask what the intended deployment scenario is and determine whether the recommendation matches that scenario.

For example, repeated customer records could justify group splitting in one project but not necessarily in another if the model is specifically intended to personalize predictions for existing customers.

---

Q19. What makes this project different from simply writing a wrapper around scikit-learn?

Answer:

The core idea is not merely wrapping an existing function.

The project separates:

Dataset analysis
       ↓
Strategy recommendation
       ↓
Split execution
       ↓
Validation

The emphasis is on making evaluation design explainable and structurally aware.

The package therefore treats splitting as a decision problem rather than only an API call.

---

Q20. What is the biggest limitation of the current project?

Answer:

The recommendation system is heuristic and the project is still in alpha.

It can identify useful structural signals, but it cannot understand every domain-specific reason why a particular evaluation protocol may be appropriate.

Also, split-level controls cannot eliminate every form of data leakage.

---

Q21. If the model gets 99% accuracy after using SplitPilot, does that mean the model is good?

Answer:

Not necessarily.

SplitPilot can help make the evaluation split more appropriate, but model quality still depends on the entire experimental pipeline.

I would investigate:

• the deployment scenario
• target construction
• feature leakage
• preprocessing
• duplicates
• class imbalance
• appropriate evaluation metrics
• temporal and group boundaries

The split is an important part of evaluation validity, but it is not the entire evaluation protocol.

---

Q22. How would you explain the project to a non-technical interviewer?

Answer:

Imagine testing a student using questions from a chapter they already memorized.

The score might be high, but it would not tell us how well they handle new material.

Machine-learning datasets can have a similar problem.

If the same customer or future information appears on both sides of the evaluation, the score may not represent the real problem.

SplitPilot helps design the test so that the evaluation better matches the question we actually want to answer.

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Q23. What software-engineering concepts does this project demonstrate?

Answer:

The project demonstrates:

• Python package structure
• modular architecture
• dataclasses
• input validation
• exception handling
• unit testing with pytest
• dependency management
• Git version control
• semantic project organization
• documentation
• package metadata through pyproject.toml

It also demonstrates translating machine-learning methodology into reusable software.

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Q24. What would you say if an interviewer asks whether this is "just a small utility"?

Answer:

I would agree that the current implementation is relatively focused, but the problem it addresses is fundamental to machine-learning experimentation.

The value is not in the number of lines of code. It is in formalizing an evaluation decision that is often handled informally.

The project also gives me a foundation for extending the system into automated split diagnostics, leakage analysis, richer recommendations, and reproducible evaluation reports.

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Q25. What did you learn from building SplitPilot?

Answer:

The main lesson was that machine-learning engineering is not only about model algorithms.

The reliability of an experiment also depends on how the data is prepared and evaluated.

Building SplitPilot made me think more carefully about assumptions behind train/test splitting, software interfaces, validation, testing, package structure, and how to communicate technical decisions clearly.

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22. Contributing

Contributions are welcome.

A typical development workflow is:

git clone https://github.com/krishgupta129/splitpilot.git
cd splitpilot
pip install -e ".[dev]"
pytest tests/test_splitter.py -v

Before opening a pull request:

1. Add or update tests for behavioral changes.
2. Run the test suite.
3. Update documentation where necessary.
4. Keep changes focused.
5. Explain the reasoning behind non-obvious implementation decisions.

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23. License

SplitPilot is distributed under the MIT License.

See LICENSE for the full license text.

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Project Philosophy

SplitPilot is built around a simple principle:

A model evaluation is only as meaningful as the assumptions behind its evaluation protocol.

A train/test split is not merely a line of preprocessing code.

It is a statement about what the model is expected to generalize to:

Random split
    → independent observations

Group split
    → unseen entities

Time split
    → future observations

Group-time split
    → future observations from structurally isolated groups

SplitPilot aims to make those assumptions visible, testable, and reusable.