5 · Data Preprocessing & Feature Engineering · Dimensionality Reduction

UMAP for visualisation

UMAP for visualisation is a communication and diagnostic technique that maps data or results into a visual form. A useful visual makes comparisons easy, exposes uncertainty and supports the analytical question rather than merely decorating the report.

Reference lessonPython exampleVisual explanation

Intuition first

What this concept means in practice

The practical value of UMAP for visualisation comes from understanding both the transformation and the boundary around it: what information is allowed to enter, what assumption is being made, and how you know the result is still valid after the transformation.

A beginner-friendly way to reason about it is to start with a tiny case where the correct result can be checked independently. Once the mechanism is clear, scale the exact same reasoning to larger tables, pipelines or models.

PurposeUse whenever a visual comparison communicates structure more clearly than a table or sentence.

MechanismChoose the comparison the reader must make, encode it with a perceptually appropriate mark/scale, label the important context, and remove elements that do not support interpretation.

EvidenceInspect intermediate and final output; compare with an independent expectation.

Main cautionTruncated axes, excessive colours and decorative 3D effects can distort interpretation.

Mechanism

Trace the operation from input to decision

Choose the comparison the reader must make, encode it with a perceptually appropriate mark/scale, label the important context, and remove elements that do not support interpretation.

1Input→

2Apply rule→

3Inspect state→

4Validate→

5Use result

Key rule

Question → comparison → visual encoding → annotation → interpretation

Visual explanation

Make the structure visible

The interactive view uses a concept-specific plot when the topic maps naturally to one; otherwise it uses a workflow view instead of leaving a broken placeholder.

Loading visual…

Practical example

Where you would use it

Use a line chart for a time trend, a scatterplot for association and a distribution plot when spread and skew matter.

Use when

Use whenever a visual comparison communicates structure more clearly than a table or sentence.

Pitfall

What can make the result misleading

Watch out

Truncated axes, excessive colours and decorative 3D effects can distort interpretation.

A useful diagnostic question is: Could the same code still run successfully if the analytical assumption were wrong? If yes, add an explicit validation check rather than relying on execution success.

Implementation

Miniature Python example

Keep the example small enough that you can inspect each stage manually.

Python

# Step 1 — Import only the named objects needed by the following steps, keeping dependencies explicit.
from sklearn.datasets import load_iris

# Step 2 — Compute the right-hand expression and store its result in `X, y` for the next step.
X, y = load_iris(return_X_y=True)
# Step 3 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 1 · original shape:", X.shape)
# Step 4 — Start the operation that may fail so the expected exception can be handled explicitly.
try:
    # Step 5 — Import only the named objects needed by the following steps, keeping dependencies explicit.
    from umap import UMAP
    # Step 6 — Fit the transformation on the training input and immediately transform that same input.
    Z = UMAP(n_components=2, n_neighbors=15, min_dist=.1, random_state=42).fit_transform(X)
    # Step 7 — Display the current value explicitly so the result/state can be inspected during execution.
    print("STEP 2 · embedding shape:", Z.shape)
    # Step 8 — Display the current value explicitly so the result/state can be inspected during execution.
    print("STEP 3 · first point:", Z[0].round(2).tolist())
# Step 9 — Handle the expected failure path instead of allowing the program to terminate unexpectedly.
except ImportError:
    # Step 10 — Display the current value explicitly so the result/state can be inspected during execution.
    print("Install the correct package first: pip install umap-learn")

Expected / illustrative output

STEP 1 · original shape: (150, 4)
STEP 2 · embedding shape: (150, 2)
STEP 3 · first point: [representative 2D coordinates]

Implementation checklist

Before you move on

Can you state what data or object enters the operation?
Can you explain what changes and what must remain invariant?
Have you checked the result on a tiny case you can verify independently?
Have you considered the main failure mode described above?
Can the operation be reproduced from code/formulas and documented assumptions?