Stage 2: Machine Learning¶

2 Learn how models learn from data and how evaluation can lie.

Goal¶

Develop the ML habits that remain essential for deep learning, LLM applications, and agent systems: baselines, splits, metrics, leakage checks, and error analysis.

Roadmap to Master This Stage¶

Read the stage goal and diagram before opening the parts.
Move through the parts in order unless you can already pass the exit criteria.
Study each sub-part folder: overview, deep dive, and examples/practice.
Build the stage artifact in small slices and measure the listed metrics.
Use the part exam after each part, or open the global Exam tab to test across the roadmap.

Stage Structure Diagram¶

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flowchart LR
  P1["<b>2.1</b><br/>Problem Framing"]
  P2["<b>2.2</b><br/>Splits and Leakage"]
  P1 --> P2
  P3["<b>2.3</b><br/>Supervised Models"]
  P2 --> P3
  P4["<b>2.4</b><br/>Metrics and Error<br/>Analysis"]
  P3 --> P4
  P5["<b>2.5</b><br/>Unsupervised<br/>Representations"]
  P4 --> P5

Parts¶

Part	Simple explanation	Build focus
2.1 Problem Framing	Turn product goals into learnable tasks with clear targets, labels, features, and constraints.	Create a task framing document.
2.2 Splits and Leakage	Design evaluation splits that represent future use instead of memorized training data.	Create and justify two split strategies.
2.3 Supervised Models	Use simple and strong supervised models before neural complexity.	Train linear, tree, and ensemble models for the same task.
2.4 Metrics and Error Analysis	Understand model behavior through metrics, thresholds, slices, and concrete failures.	Create an evaluation report with examples.
2.5 Unsupervised Representations	Prepare for embeddings, clustering, retrieval, and topic discovery.	Cluster and visualize a dataset.

Sub-Part Map¶

Part	Sub-part	Why it matters
2.1	2.1.1 Targets Labels and Features	Targets Labels and Features is the working skill inside Problem Framing that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result.
2.1	2.1.2 Prediction Time Availability	Prediction Time Availability is the working skill inside Problem Framing that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result.
2.1	2.1.3 Label Noise and Annotation Quality	Label Noise and Annotation Quality is the working skill inside Problem Framing that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result.
2.1	2.1.4 Baseline Definition	Baseline Definition is the working skill inside Problem Framing that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result.
2.2	2.2.1 Train Validation and Test Sets	Train Validation and Test Sets is the working skill inside Splits and Leakage that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result.
2.2	2.2.2 Time and Group Splits	Time and Group Splits is the working skill inside Splits and Leakage that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result.
2.2	2.2.3 Data Leakage Patterns	Data Leakage Patterns is the working skill inside Splits and Leakage that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result.
2.2	2.2.4 Preprocessing Without Leakage	Preprocessing Without Leakage is the working skill inside Splits and Leakage that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result.
2.3	2.3.1 Linear and Logistic Models	Linear and Logistic Models is the working skill inside Supervised Models that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result.
2.3	2.3.2 Decision Trees and Random Forests	Decision Trees and Random Forests is the working skill inside Supervised Models that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result.
2.3	2.3.3 Gradient Boosted Trees	Gradient Boosted Trees is the working skill inside Supervised Models that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result.
2.3	2.3.4 Model Pipelines and Hyperparameters	Model Pipelines and Hyperparameters is the working skill inside Supervised Models that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result.
2.3	2.3.5 Interpretability and Feature Importance	Interpretability and Feature Importance is the working skill inside Supervised Models that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result.
2.4	2.4.1 Classification Metrics	Classification Metrics is the working skill inside Metrics and Error Analysis that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result.
2.4	2.4.2 Regression and Ranking Metrics	Regression and Ranking Metrics is the working skill inside Metrics and Error Analysis that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result.
2.4	2.4.3 Calibration and Thresholds	Calibration and Thresholds is the working skill inside Metrics and Error Analysis that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result.
2.4	2.4.4 Slice Based Error Analysis	Slice Based Error Analysis is the working skill inside Metrics and Error Analysis that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result.
2.5	2.5.1 Clustering and Similarity	Clustering and Similarity is the working skill inside Unsupervised Representations that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result.
2.5	2.5.2 Dimensionality Reduction	Dimensionality Reduction is the working skill inside Unsupervised Representations that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result.
2.5	2.5.3 Embeddings as Representations	Embeddings as Representations is the working skill inside Unsupervised Representations that helps you build the stage artifact, An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card, while collecting enough evidence to trust the result.

Stage Artifact¶

An ML baseline report comparing simple and stronger models with metrics, error slices, and a model card.

What to Measure¶

baseline metric
improved model metric
train-validation gap
three error slices
leakage checklist

Exit Criteria¶

frame supervised and unsupervised tasks
choose and interpret metrics
detect leakage and evaluation flaws
write a concise model card and failure analysis

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