Semi-supervised learning basics in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the semi-supervised learning module from scikit-learn.

ML Python

from sklearn import [1]

Drag options to blanks, or click blank then click option'

Asemi_supervised

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'semi-supervised' with a dash instead of underscore.

Trying to import from 'supervised' or 'unsupervised' instead.

The semi-supervised learning module in scikit-learn is called 'semi_supervised'.

2fill in blank

medium

Complete the code to create a LabelPropagation model instance.

ML Python

model = [1]()

Drag options to blanks, or click blank then click option'

ALabelPropagation

BLabelSpreading

CKNeighborsClassifier

DRandomForestClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Using supervised classifiers like KNeighborsClassifier or RandomForestClassifier.

Confusing LabelPropagation with LabelSpreading.

LabelPropagation is a common semi-supervised learning model in scikit-learn.

3fill in blank

hard

Fix the error in the code to fit the semi-supervised model with data X and labels y, where unlabeled points are marked as -1.

ML Python

model.fit([1], y)

Drag options to blanks, or click blank then click option'

Amodel

By

CX

Dunlabeled

Attempts:

3 left

💡 Hint

Common Mistakes

Passing labels y as the first argument instead of X.

Passing the model itself or an undefined variable.

The fit method requires the feature data X as the first argument.

4fill in blank

hard

Fill both blanks to create a dictionary of predicted labels for unlabeled data points.

ML Python

predictions = {i: model.[1]()[i] for i in range(len([2])) if y[i] == -1}

Drag options to blanks, or click blank then click option'

Apredict

BX

Cfit

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'fit' or 'score' instead of 'predict'.

Iterating over y instead of X.

We use model.predict() to get predicted labels and iterate over X to find unlabeled points.

5fill in blank

hard

Fill all three blanks to create a semi-supervised learning pipeline with LabelSpreading, fit it, and predict labels.

ML Python

from sklearn.semi_supervised import [1]
model = [2](kernel=[3])
model.fit(X, y)
predicted_labels = model.predict(X)

Drag options to blanks, or click blank then click option'

ALabelSpreading

B'rbf'

C'linear'

DLabelPropagation

Attempts:

3 left

💡 Hint

Common Mistakes

Using LabelPropagation instead of LabelSpreading.

Using 'linear' kernel which is less common here.

LabelSpreading is imported and instantiated with kernel='rbf' for smooth label spreading.

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