Feature Engineering — Scaling, Encoding & Leakage Avoidance — Hands-on

Learn how to turn messy biomedical and omics data into robust, model ready features. This module covers scaling, transformations, encoding strategies and systematic leakage avoidance so that your train/validation/test pipelines remain honest, reproducible and deployment ready in R and Python.

Feature Engineering — Scaling, Encoding & Leakage Avoidance

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Session Index

Session 1 — Fundamentals of Features & Data Representation Session 2 — Scaling, Transformation & Normalization Session 3 — Encoding Categorical & Structured Data Session 4 — Leakage Avoidance & Safe ML Pipelines

Session 1

Fee: Rs 8800

Fundamentals of Features & Data Representation

From raw measurements to features

wide vs long omics tables targets, covariates, IDs feature types and roles

Numeric, categorical and ordinal variables

continuous vs discrete ordinal scales date/time and time since

Feature quality checks

missingness patterns low variance and constants correlated and redundant features

Session 2

Fee: Rs 11800

Scaling, Transformation & Normalization

Why scaling matters for ML algorithms

distance based vs tree based models gradient descent stability clinical interpretability

Common scaling and transformation choices

standardization and min max robust and quantile transforms log, Box Cox, Yeo Johnson

Omics specific normalization ideas

library size concepts z scores and ranks batch aware scaling caveats

Session 3

Fee: Rs 14800

Encoding Categorical & Structured Data

Basic encoders and when to use them

one hot and dummy coding ordinal encoding hashing tricks

Advanced encoders for high cardinality data

target and impact encoding frequency and likelihood encoders leakage risks with target encoders

Dates, times and grouped features

cyclical encodings aggregations per patient or sample group level summaries

Session 4

Fee: Rs 18800

Leakage Avoidance & Safe ML Pipelines

What is data leakage and how it arises

preprocessing on full dataset vs training fold

Safe splitting and cross validation practices

train/validation/test protocols pipeline objects and column transformers grouped and patient level splits

Deliverables: leak safe feature pipeline

R recipes and tidymodels Python scikit learn pipelines documented transformation map

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Feature Engineering — Scaling, Encoding & Leakage Avoidance Training | Biostatistics & ML for Omics

Feature Engineering — Scaling, Encoding & Leakage Avoidance — Hands-on