Semi-supervised learning basics in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the main advantage of semi-supervised learning?

Semi-supervised learning uses both labeled and unlabeled data. What is the main advantage of this approach compared to supervised learning?

A It does not need any data preprocessing.

B It always achieves higher accuracy than supervised learning.

C It only works with unlabeled data.

D It requires fewer labeled examples, reducing labeling cost.

💡 Hint

Think about the cost and effort of labeling data.

Semi-supervised learning reduces the need for many labeled examples by leveraging unlabeled data, which is usually easier and cheaper to collect.

❓ Predict Output

intermediate

Output of semi-supervised label propagation

Given the following Python code using label propagation, what is the predicted label for the unlabeled point?

ML Python

from sklearn.semi_supervised import LabelPropagation
import numpy as np

# Data points: 3 labeled, 1 unlabeled
X = np.array([[1, 2], [2, 3], [3, 4], [8, 9]])
# Labels: 0, 0, 1, -1 (unlabeled)
y = np.array([0, 0, 1, -1])

model = LabelPropagation()
model.fit(X, y)
predicted_label = model.transduction_[-1]

A Raises an error

B 0

C -1

D 1

💡 Hint

Label propagation assigns labels based on neighbors' labels.

The unlabeled point [8,9] is far from points labeled 0 and 1, but label propagation assigns it the label of the closest cluster, which is 0 in this case.

❓ Model Choice

advanced

Choosing a model for semi-supervised learning

You have a small labeled dataset and a large unlabeled dataset. Which model is best suited for semi-supervised learning in this scenario?

A Support Vector Machine with self-training

B Standard supervised Random Forest

C K-Means clustering

D Linear Regression

💡 Hint

Look for a model that can iteratively label unlabeled data.

Support Vector Machine with self-training can use labeled data to train, then label unlabeled data iteratively, making it suitable for semi-supervised learning.

❓ Hyperparameter

advanced

Key hyperparameter in label spreading

In label spreading, which hyperparameter controls how much the model trusts the initial labels versus the structure of the unlabeled data?

A Alpha (clamping factor)

B Learning rate

C Number of neighbors

D Regularization strength

💡 Hint

This parameter balances label retention and propagation.

Alpha controls the clamping factor: a higher alpha means more trust in the initial labels, while a lower alpha means more trust in the unlabeled data structure.

❓ Metrics

expert

Evaluating semi-supervised learning performance

You trained a semi-supervised model. Which metric is most appropriate to evaluate its performance on the labeled test set?

A Mean squared error

B Silhouette score

C Accuracy

D Adjusted Rand index

💡 Hint

Consider that you have true labels for the test set.

Accuracy measures how many test samples are correctly classified, which is suitable when true labels are available.

Practice

1. What is the main idea behind semi-supervised learning in machine learning?

easy

A. Using only unlabeled data to train a model

B. Using only labeled data to train a model

C. Using both labeled and unlabeled data to train a model

D. Training multiple models independently

Solution

1. Step 1: Understand the data types in semi-supervised learning

   Semi-supervised learning uses a mix of labeled and unlabeled data to improve model training.

2. Step 2: Compare options with the definition

   Using both labeled and unlabeled data to train a model correctly states the use of both labeled and unlabeled data, unlike other options which mention only one type or unrelated concepts.

3. Final Answer:

   Using both labeled and unlabeled data to train a model -> Option C

4. Quick Check:

   Semi-supervised learning = labeled + unlabeled data [OK]

Hint: Remember: semi-supervised = mix of labeled and unlabeled [OK]

Common Mistakes:

• Confusing semi-supervised with supervised learning
• Thinking it uses only unlabeled data
• Assuming it trains multiple models separately

2. Which of the following is a common method used in semi-supervised learning?

easy

A. Self-training

B. Gradient boosting

C. K-means clustering

D. Decision trees

Solution

1. Step 1: Identify methods specific to semi-supervised learning

   Self-training is a popular semi-supervised method where the model labels unlabeled data iteratively.

2. Step 2: Eliminate unrelated methods

   Gradient boosting and decision trees are supervised learning methods; K-means is unsupervised clustering, not semi-supervised.

3. Final Answer:

   Self-training -> Option A

4. Quick Check:

   Semi-supervised method = Self-training [OK]

Hint: Look for methods that use model to label unlabeled data [OK]

Common Mistakes:

• Confusing supervised methods as semi-supervised
• Choosing clustering as semi-supervised
• Not knowing self-training meaning

3. Consider this Python snippet using label spreading for semi-supervised learning:

from sklearn.semi_supervised import LabelSpreading
import numpy as np

X = np.array([[1], [2], [3], [4], [5]])
y = np.array([0, 1, -1, -1, -1])  # -1 means unlabeled

model = LabelSpreading()
model.fit(X, y)
preds = model.transduction_
print(preds)

What will be the output printed by print(preds)?

medium

A. [0 1 0 0 0]

B. [1 1 1 1 1]

C. [0 1 -1 -1 -1]

D. [0 1 1 1 1]

Solution

1. Step 1: Understand label spreading behavior

   Label spreading propagates labels from labeled points (0 and 1) to unlabeled points (-1) based on similarity.

2. Step 2: Predict labels for unlabeled points

   Since points 2,3,4 are close to labeled point 1, they get label 1. Points 0 and 1 keep their labels 0 and 1.

3. Final Answer:

   [0 1 1 1 1] -> Option D

4. Quick Check:

   Label spreading fills unlabeled with nearest labels [OK]

Hint: Label spreading fills unlabeled with nearest known labels [OK]

Common Mistakes:

• Assuming unlabeled points remain -1
• Thinking labels spread to 0 instead of 1
• Confusing output with input labels

4. The following code attempts to use self-training but has an error:

from sklearn.semi_supervised import SelfTrainingClassifier
from sklearn.svm import SVC

X = [[1], [2], [3], [4]]
y = [0, 1, -1, -1]

base_model = SVC()
model = SelfTrainingClassifier(base_model)
model.fit(X, y)

What is the error in this code?

medium

A. Labels cannot contain -1 for unlabeled data

B. SVC requires probability=True for self-training

C. X must be a numpy array, not a list

D. SelfTrainingClassifier cannot use SVC as base model

Solution

1. Step 1: Check requirements for SelfTrainingClassifier base model

   SelfTrainingClassifier needs base model to provide probability estimates, so SVC must be initialized with probability=True.

2. Step 2: Identify the missing argument

   The code uses default SVC without probability=True, causing an error during fit.

3. Final Answer:

   SVC requires probability=True for self-training -> Option B

4. Quick Check:

   SelfTrainingClassifier needs probabilistic base model [OK]

Hint: Remember: SVC needs probability=True for self-training [OK]

Common Mistakes:

• Thinking -1 labels are invalid
• Believing lists can't be used as input
• Assuming SVC can't be base model

5. You have a dataset with 1000 samples but only 50 are labeled. You want to improve model accuracy using semi-supervised learning. Which approach is best to start with?

hard

A. Use self-training with a base classifier that predicts labels on unlabeled data iteratively

B. Ignore unlabeled data and train only on 50 labeled samples

C. Use unsupervised clustering to label all data without any model

D. Label all 950 samples manually before training

Solution

1. Step 1: Understand the problem with few labeled samples

   With only 50 labeled samples, training a model directly may not generalize well.

2. Step 2: Choose a semi-supervised method to leverage unlabeled data

   Self-training uses the base classifier to label unlabeled data iteratively, improving learning without costly manual labeling.

3. Final Answer:

   Use self-training with a base classifier that predicts labels on unlabeled data iteratively -> Option A

4. Quick Check:

   Semi-supervised learning improves with self-training on unlabeled data [OK]

Hint: Start with self-training to use unlabeled data effectively [OK]

Common Mistakes:

• Ignoring unlabeled data wastes valuable information
• Assuming manual labeling is always feasible
• Confusing clustering with semi-supervised learning