Semi-supervised learning basics in ML Python - Model Metrics & Evaluation

Metrics & Evaluation - Semi-supervised learning basics

Which metric matters for Semi-supervised learning and WHY

Semi-supervised learning uses both labeled and unlabeled data. The key metric depends on the task, often classification accuracy, precision, recall, or F1 score. We focus on metrics that show how well the model learns from limited labels and generalizes to new data. For example, if the goal is to find rare cases, recall is important. If avoiding false alarms matters, precision is key. Accuracy alone can be misleading if classes are imbalanced.

Confusion matrix example

       Predicted
       Pos   Neg
Actual Pos  40    10
       Neg  15    35

Total samples = 40 + 10 + 15 + 35 = 100

Precision = TP / (TP + FP) = 40 / (40 + 15) = 0.727
Recall = TP / (TP + FN) = 40 / (40 + 10) = 0.8
F1 = 2 * (0.727 * 0.8) / (0.727 + 0.8) ≈ 0.761
Accuracy = (TP + TN) / Total = (40 + 35) / 100 = 0.75

Precision vs Recall tradeoff with examples

In semi-supervised learning, the model may guess labels for unlabeled data. If it guesses too many positives, precision drops (more false alarms). If it guesses too few, recall drops (misses real positives).

Example 1: Detecting spam emails. High precision means few good emails marked as spam. Better to avoid false alarms, so precision matters more.

Example 2: Detecting diseases. High recall means catching most sick patients. Missing a sick patient is worse, so recall matters more.

Good vs Bad metric values for Semi-supervised learning

Good: Balanced precision and recall above 0.7, F1 score above 0.7, accuracy reflecting true performance on labeled and unlabeled data.

Bad: High accuracy but very low recall or precision, indicating the model ignores minority classes or guesses poorly on unlabeled data.

Common pitfalls in metrics for Semi-supervised learning

• Accuracy paradox: High accuracy can hide poor performance on rare classes.
• Data leakage: Using unlabeled data incorrectly can leak test info, inflating metrics.
• Overfitting: Model fits labeled data too closely but fails on unlabeled data, causing misleading metrics.

Self-check question

Your semi-supervised model has 98% accuracy but only 12% recall on the positive class (rare cases). Is it good for production? Why or why not?

Answer: No, it is not good. The model misses most positive cases (low recall), which is critical if those cases matter. High accuracy is misleading because negatives dominate the data.

Key Result

In semi-supervised learning, balanced precision and recall are key to ensure the model learns well from limited labels and generalizes properly.

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