bio-machine-learning-biomarker-discovery

Boruta All-Relevant Selection

Identifies all features that are significantly better than random (shadow features).

from boruta import BorutaPy
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
import numpy as np

rf = RandomForestClassifier(n_estimators=100, n_jobs=-1, random_state=42)

## mRMR (Minimum Redundancy Maximum Relevance)

Selects features that are individually relevant but minimally redundant with each other.

```python
from mrmr import mrmr_classif

## LASSO Feature Selection

L1 regularization drives irrelevant coefficients to zero.

```python
from sklearn.linear_model import LassoCV
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)