ml4t-walk-forward-cv skillA
ml4t-walk-forward-cv is agent-read markdown (skill) from ml4t/skills: Rolling or expanding window CV that preserves temporal order. Use when evaluating ML models on time-series data where standard k-fold causes temporal leakage..
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
# Walk-Forward Cross-Validation
A single train/test split tells you nothing about how a model adapts over time. Walk-forward CV slides a window through the data, training and testing sequentially, revealing how performance evolves across market regimes.
## The Problem
A single 80/20 train/test split produces one score from one market period. The model may excel in bull markets but fail in drawdowns - you cannot tell. Standard k-fold shuffles time, leaking future data. You need sequential evaluation that mirrors live deployment: train on the past, predict the future, advance, repeat. This exposes regime sensitivity and stationarity failures.
## The Pattern
### WRONG
```python
from sklearn.model_selection import train_test_split
# Single split - one market regime, no adaptation signal
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, shuffle=True # Shuffling leaks future
)
model.fit(X_train, y_train)
print(f"Score: {model.score(X_test, y_test):.3f}") # One number
```
### CORRECT
```python
import numpy as np
from sklearn.model_selection import TimeSeriesSplit
# Walk-forward with gap for label horizon
n_splits = 5
…Read the whole file at its exact version.
How to install
mdr add ml4t/skills/ml4t-walk-forward-cv@git:20260901.c415df0mdr add ml4t/skills/ml4t-walk-forward-cv@sha256:230a9d26991b4bcfPin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_ljsext264xbfhttw)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260901.c415df0 latest | 2026-09-01 | c415df0 | 4,387 B | A | view · diff |
| git:20260528.303089e | 2026-05-28 | 303089e | 4,397 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (4387 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
ml4t/skills · 19 stars · license Apache-2.0 · pushed 2026-09-24 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_ljsext264xbfhttw GET https://markdownregistry.com/api/v1/resolve?ref=ml4t/skills/ml4t-walk-forward-cv GET https://markdownregistry.com/api/v1/blob/230a9d26991b4bcf4d3694e02942c5e997ad023783a07b56a956986d1290756c
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