Support Vector Machine (SVM) in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the main goal of a Support Vector Machine?

In simple terms, what does a Support Vector Machine try to do when it learns from data?

A It tries to group data points into clusters without using labels.

B It tries to find the line or boundary that best separates different groups with the widest gap.

C It tries to reduce the number of features by combining them.

D It tries to memorize all the training data points exactly to predict future data.

❓ Predict Output

intermediate

What is the output of this SVM prediction code?

Given this Python code using sklearn's SVM, what will be printed?

ML Python

from sklearn import svm
X = [[0, 0], [1, 1]]
y = [0, 1]
clf = svm.SVC(kernel='linear')
clf.fit(X, y)
pred = clf.predict([[2, 2]])
print(pred[0])

A 1

B 0

C [1]

D Error: kernel not supported

❓ Hyperparameter

advanced

Which SVM hyperparameter controls the trade-off between margin size and classification error?

In SVM, which parameter lets you decide how much you want to avoid misclassifying training points versus having a wider margin?

A C

B Gamma

C Kernel

D Degree

❓ Metrics

advanced

Which metric is most suitable to evaluate an SVM classifier on imbalanced data?

If your SVM model predicts classes but one class is much rarer than the other, which metric gives a better sense of performance?

A Recall

B Accuracy

C F1 Score

D Precision

🔧 Debug

expert

What error does this SVM code raise?

Consider this code snippet. What error will it raise when run?

ML Python

from sklearn import svm
X = [[0, 0], [1, 1], [2, 2]]
y = [0, 1]
clf = svm.SVC(kernel='linear')
clf.fit(X, y)

A IndexError: list index out of range

B TypeError: kernel must be a string

C No error, model fits successfully

D ValueError: Found input variables with inconsistent numbers of samples