Foundations · Learning Paradigms

Semi-supervised learning

Semi-supervised learning combines a small labelled set with a larger unlabelled set. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.

Reference lessonPython exampleVisual explanation

Intuition first

What this concept means in practice

The practical value of Semi-supervised learning 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 when labels are expensive but unlabelled data are plentiful and drawn from a similar distribution.

MechanismUse labelled examples for supervised loss and exploit unlabelled examples through consistency, pseudo-labels or graph structure.

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

Main cautionIncorrect pseudo-labels can reinforce model errors, especially under distribution shift.

Mechanism

Trace the operation from input to decision

Use labelled examples for supervised loss and exploit unlabelled examples through consistency, pseudo-labels or graph structure.

1Input→

2Apply rule→

3Inspect state→

4Validate→

5Use result

Key rule

Labeled loss + λ × unlabeled objective

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.

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Practical example

Where you would use it

Classify medical images when only a subset has specialist annotations.

Use when

Use when labels are expensive but unlabelled data are plentiful and drawn from a similar distribution.

Pitfall

What can make the result misleading

Watch out

Incorrect pseudo-labels can reinforce model errors, especially under distribution shift.

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

# Purpose: demonstrate Semi-supervised learning with a small, inspectable example.
# Follow the comments and printed stages to connect each operation with its result.
# Import the library or helper used in this example.
# Step 1 — Import only the named objects needed by the following steps, keeping dependencies explicit.
from sklearn.datasets import make_classification
# Import the library or helper used in this example.
# Step 2 — Import only the named objects needed by the following steps, keeping dependencies explicit.
from sklearn.linear_model import LogisticRegression
# Import the library or helper used in this example.
# Step 3 — Import only the named objects needed by the following steps, keeping dependencies explicit.
from sklearn.model_selection import train_test_split
# Import the library or helper used in this example.
# Step 4 — Import only the named objects needed by the following steps, keeping dependencies explicit.
from sklearn.metrics import accuracy_score

# Store this intermediate value with a descriptive name for the next step.
# Step 5 — Compute the right-hand expression and store its result in `X,y` for the next step.
X,y=make_classification(n_samples=80,n_features=5,n_informative=3,random_state=7)
# Separate training and evaluation data before fitting the model.
# Step 6 — Split examples into separate development/evaluation partitions before any leakage-prone fitting occurs.
Xtr,Xte,ytr,yte=train_test_split(X,y,test_size=.25,stratify=y,random_state=7)
# Print this intermediate result so you can verify the workflow step by step.
# Step 7 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 1 · Train/test:",Xtr.shape,Xte.shape)
# Configure the estimator or pipeline with the chosen settings.
# Step 8 — Fit the model or transformer, learning its parameters from the supplied training data.
model=LogisticRegression(max_iter=500).fit(Xtr,ytr)
# Print this intermediate result so you can verify the workflow step by step.
# Step 9 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 2 · Model trained; coefficient shape:",model.coef_.shape)
# Generate predictions from the fitted model.
# Step 10 — Generate predictions using the already-fitted model.
p=model.predict(Xte)
# Print this intermediate result so you can verify the workflow step by step.
# Step 11 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 3 · Test accuracy:",round(accuracy_score(yte,p),3))
# Print this intermediate result so you can verify the workflow step by step.
# Step 12 — Display the current value explicitly so the result/state can be inspected during execution.
print("STEP 3 · First predictions:",p[:6].tolist())

Expected / illustrative output

STEP 1 · Train/test: (60, 5) (20, 5)
STEP 2 · Model trained; coefficient shape: (1, 5)
STEP 3 · Test accuracy: 0.8
STEP 3 · First predictions: [1, 0, 0, 0, 1, 0]

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?