ml4t-feature-selection skillA
ml4t-feature-selection is agent-read markdown (skill) from ml4t/skills: Select informative features using IC ranking, mutual information, or RFE - always within CV folds. Use when reducing feature dimensionality before training..
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
# Feature Selection
Selecting features on the full dataset is a form of lookahead bias. The test set influences which features are kept, inflating out-of-sample performance. Feature selection must happen inside each CV fold.
## The Problem
With 50 features and a finite sample, some will correlate with forward returns by chance alone. If you rank features by IC on the full dataset and keep the top 10, those 10 are partly selected for noise. Out-of-sample, the noise component vanishes and the model underperforms expectations.
## The Pattern
### WRONG
```python
from scipy.stats import spearmanr
import numpy as np
# Feature selection on full dataset - leaks test-set information
ic_scores = {col: abs(spearmanr(X[col], y).statistic) for col in X.columns}
selected = sorted(ic_scores, key=ic_scores.get, reverse=True)[:20]
model.fit(X[selected], y)
```
### CORRECT
```python
from scipy.stats import spearmanr
from sklearn.model_selection import TimeSeriesSplit
import numpy as np
tscv = TimeSeriesSplit(n_splits=5)
for train_idx, test_idx in tscv.split(X):
X_train, y_train = X[train_idx], y[train_idx]
# Select features using ONLY training data
ic_scores = {
…Read the whole file at its exact version.
How to install
mdr add ml4t/skills/ml4t-feature-selection@git:20260901.c415df0mdr add ml4t/skills/ml4t-feature-selection@sha256:d05ffa12dcf951f1Pin 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_xfeenp5gjey73osi)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260901.c415df0 latest | 2026-09-01 | c415df0 | 4,748 B | A | view · diff |
| git:20260528.303089e | 2026-05-28 | 303089e | 4,764 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 (4748 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_xfeenp5gjey73osi GET https://markdownregistry.com/api/v1/resolve?ref=ml4t/skills/ml4t-feature-selection GET https://markdownregistry.com/api/v1/blob/d05ffa12dcf951f1a57f012b17223f30dbe2c706c8687fc375f6122aa714a0af
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