One-vs-rest and one-vs-one strategies in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a one-vs-rest classifier using scikit-learn.

ML Python

from sklearn.multiclass import [1]
from sklearn.svm import SVC

model = [1](estimator=SVC())

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

AOneVsRestClassifier

BOneVsOneClassifier

CRandomForestClassifier

DKNeighborsClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Using OneVsOneClassifier instead of OneVsRestClassifier

Using a classifier that is not designed for multi-class wrapping

The OneVsRestClassifier wraps a binary classifier to handle multi-class problems by training one classifier per class.

2fill in blank

medium

Complete the code to create a one-vs-one classifier using scikit-learn.

ML Python

from sklearn.multiclass import [1]
from sklearn.svm import SVC

model = [1](estimator=SVC())

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

AOneVsRestClassifier

BLogisticRegression

CDecisionTreeClassifier

DOneVsOneClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing OneVsRestClassifier with OneVsOneClassifier

Using a base estimator that is not compatible

The OneVsOneClassifier trains one classifier for every pair of classes, useful for multi-class classification.

3fill in blank

hard

Fix the error in the code to correctly train a one-vs-rest classifier.

ML Python

from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import SVC

model = OneVsRestClassifier(SVC())
model.[1](X_train, y_train)

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

Apredict

Bfit

Ctrain

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'train' instead of 'fit' to train the model

Using 'predict' or 'score' methods for training

The method to train a scikit-learn model is fit, not 'train'.

4fill in blank

hard

Fill both blanks to create a one-vs-one classifier and train it.

ML Python

from sklearn.multiclass import [1]
from sklearn.svm import SVC

model = [2](estimator=SVC())
model.fit(X_train, y_train)

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

AOneVsOneClassifier

BOneVsRestClassifier

CRandomForestClassifier

DKNeighborsClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing OneVsRestClassifier and OneVsOneClassifier

Importing one class but instantiating another

Both blanks require OneVsOneClassifier to correctly create and use the one-vs-one strategy.

5fill in blank

hard

Fill all three blanks to create a one-vs-rest classifier, train it, and predict on test data.

ML Python

from sklearn.multiclass import [1]
from sklearn.svm import SVC

model = [1](estimator=SVC())
model.[2](X_train, y_train)
predictions = model.[3](X_test)

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

AOneVsRestClassifier

Bfit

Cpredict

DOneVsOneClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Using OneVsOneClassifier instead of OneVsRestClassifier

Using 'train' instead of 'fit'

Using 'fit' instead of 'predict' for predictions

Use OneVsRestClassifier to create the model, fit to train it, and predict to get predictions.

Practice

1. What is the main idea behind the one-vs-rest strategy in multi-class classification?

easy

A. Train one model per class to separate that class from all others combined.

B. Train one model for every pair of classes.

C. Train a single model to classify all classes at once.

D. Train models only for the most frequent classes.

Solution

1. Step 1: Understand one-vs-rest approach

   One-vs-rest means creating one model per class. Each model learns to separate its class from all other classes combined.

2. Step 2: Compare with other options

   One-vs-one trains models for every pair, not per class. Single model for all classes is not one-vs-rest. Training only on frequent classes is unrelated.

3. Final Answer:

   Train one model per class to separate that class from all others combined. -> Option A

4. Quick Check:

   One-vs-rest = One model per class [OK]

Hint: One-vs-rest means one model per class vs all others [OK]

Common Mistakes:

• Confusing one-vs-rest with one-vs-one
• Thinking one-vs-rest uses one model for all classes
• Assuming one-vs-rest trains only on frequent classes

2. Which of the following correctly describes the number of models trained in the one-vs-one strategy for a problem with 4 classes?

easy

A. 4 models

B. 6 models

C. 1 model

D. 8 models

Solution

1. Step 1: Calculate number of pairs for 4 classes

   One-vs-one trains a model for every pair of classes. Number of pairs = 4 choose 2 = 4*3/2 = 6.

2. Step 2: Verify other options

   4 models is one per class (one-vs-rest). 1 model is single multi-class. 8 models is incorrect count.

3. Final Answer:

   6 models -> Option B

4. Quick Check:

   Pairs for 4 classes = 6 [OK]

Hint: Number of one-vs-one models = n*(n-1)/2 [OK]

Common Mistakes:

• Using number of classes instead of pairs
• Confusing one-vs-one with one-vs-rest counts
• Calculating pairs incorrectly

3. Consider a dataset with 3 classes: A, B, and C. Using one-vs-rest, how many models are trained and what does each model learn?

medium

A. 6 models; each separates pairs of classes.

B. 3 models; each separates one class from one other class only.

C. 1 model; separates all three classes at once.

D. 3 models; each separates one class from the other two combined.

Solution

1. Step 1: Count models in one-vs-rest for 3 classes

   One-vs-rest trains one model per class, so 3 models total.

2. Step 2: Understand model learning in one-vs-rest

   Each model learns to separate its class from all other classes combined (not just one other class).

3. Final Answer:

   3 models; each separates one class from the other two combined. -> Option D

4. Quick Check:

   One-vs-rest with 3 classes = 3 models [OK]

Hint: One-vs-rest trains one model per class vs all others [OK]

Common Mistakes:

• Thinking one-vs-rest trains models per pair
• Assuming only one model is trained
• Confusing one-vs-rest with one-vs-one

4. You implemented one-vs-one for a 5-class problem but only trained 4 models. What is the likely mistake?

medium

A. You trained models only for the most frequent classes.

B. You trained one model per class instead of pairs.

C. You forgot to train models for all pairs; should be 10 models.

D. You trained a single multi-class model.

Solution

1. Step 1: Calculate expected number of one-vs-one models for 5 classes

   Number of pairs = 5 choose 2 = 5*4/2 = 10 models expected.

2. Step 2: Identify mistake from training only 4 models

   Training only 4 models means some pairs were missed. Possibly forgot to train all pairs.

3. Final Answer:

   You forgot to train models for all pairs; should be 10 models. -> Option C

4. Quick Check:

   One-vs-one for 5 classes = 10 models [OK]

Hint: One-vs-one needs n*(n-1)/2 models; check count [OK]

Common Mistakes:

• Counting models as number of classes
• Confusing one-vs-one with one-vs-rest
• Training incomplete pairs

5. You have a 4-class problem with unbalanced data. Which strategy is better to handle this imbalance and why?

hard

A. One-vs-one, because training on pairs reduces imbalance impact between classes.

B. Neither, use a single multi-class model only.

C. One-vs-rest, because each model focuses on separating one class from all others, helping with imbalance.

D. Train only on the largest class to simplify the problem.

Solution

1. Step 1: Understand imbalance effect on one-vs-rest

   One-vs-rest models separate one class vs all others combined, which can cause imbalance if one class is small and others are large.

2. Step 2: Understand one-vs-one advantage

   One-vs-one trains models on pairs of classes, so imbalance is less severe per model, improving learning on minority classes.

3. Step 3: Evaluate other options

   Single multi-class model may struggle with imbalance. Training only on largest class ignores others.

4. Final Answer:

   One-vs-one, because training on pairs reduces imbalance impact between classes. -> Option A

5. Quick Check:

   One-vs-one handles imbalance better [OK]

Hint: One-vs-one handles imbalance better by focusing on pairs [OK]

Common Mistakes:

• Assuming one-vs-rest always better for imbalance
• Ignoring imbalance effects on combined classes
• Choosing single model ignoring class distribution