Semi-supervised learning basics in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is semi-supervised learning?

Semi-supervised learning is a type of machine learning that uses a small amount of labeled data along with a large amount of unlabeled data to train models. It helps improve learning when labeling data is expensive or time-consuming.

beginner

Why use semi-supervised learning instead of supervised learning?

Because labeling data can be costly or slow, semi-supervised learning uses many unlabeled examples to help the model learn better patterns without needing as many labeled examples.

intermediate

Name two common methods used in semi-supervised learning.

Two common methods are: 1) Self-training, where the model labels unlabeled data and retrains itself, and 2) Graph-based methods, which use connections between data points to spread label information.

beginner

What role does unlabeled data play in semi-supervised learning?

Unlabeled data helps the model understand the overall structure and distribution of the data, which improves its ability to classify or predict even with few labeled examples.

beginner

Give a real-life example where semi-supervised learning is useful.

In medical imaging, labeling images requires expert doctors and is expensive. Semi-supervised learning can use a few labeled images and many unlabeled ones to build good models for diagnosis.

What type of data does semi-supervised learning use?

A Both labeled and unlabeled data

B Only labeled data

C Only unlabeled data

D No data at all

Semi-supervised learning combines a small amount of labeled data with a large amount of unlabeled data.

Why is semi-supervised learning helpful?

A It ignores unlabeled data

B It only uses labeled data

C It requires more labeled data than supervised learning

D It reduces the need for many labeled examples

Semi-supervised learning helps when labeled data is scarce by using unlabeled data to improve learning.

Which method is NOT typically used in semi-supervised learning?

A Graph-based methods

B Reinforcement learning

C Self-training

D Label propagation

Reinforcement learning is a different type of learning and not a common semi-supervised method.

In semi-supervised learning, unlabeled data helps the model by:

A Providing information about data structure

B Replacing labeled data completely

C Confusing the model

D Being ignored during training

Unlabeled data helps the model learn the overall shape and distribution of data.

Which scenario is a good fit for semi-supervised learning?

A When only unlabeled data is available

B When there is plenty of labeled data

C When labeled data is expensive and unlabeled data is abundant

D When no data is available

Semi-supervised learning is best when labeled data is limited but unlabeled data is easy to get.

Explain what semi-supervised learning is and why it is useful.

Think about how using both labeled and unlabeled data helps when labels are hard to get.

Describe two common methods used in semi-supervised learning and how they work.

One method lets the model label data itself; the other uses connections between data points.

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