Support Vector Machine (SVM) in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the SVM classifier from scikit-learn.

ML Python

from sklearn.svm import [1]

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

ASVC

BSVR

CSVMClassifier

DLinearSVC

Attempts:

3 left

💡 Hint

Common Mistakes

Using SVR which is for regression, not classification.

Trying to import a non-existent class like SVMClassifier.

The SVC class is the standard Support Vector Classifier in scikit-learn used for classification tasks.

2fill in blank

medium

Complete the code to create an SVM model with a linear kernel.

ML Python

model = SVC(kernel=[1])

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

A'rbf'

B'poly'

C'linear'

D'sigmoid'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'rbf' or 'poly' when a linear kernel is needed.

Not putting the kernel name inside quotes.

The kernel='linear' option tells the SVM to use a linear decision boundary.

3fill in blank

hard

Fix the error in the code to train the SVM model on data X and labels y.

ML Python

model = SVC()
model.[1](X, y)

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

Atrain

Btransform

Cpredict

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using train which is not a valid method.

Confusing predict with training.

The fit method trains the model on the data and labels.

4fill in blank

hard

Fill both blanks to create a dictionary of predictions for each sample in X_test.

ML Python

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

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

Apredict

Bi

C0

DX_test

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform instead of predict.

Using a fixed index like 0 instead of the loop variable.

We use predict to get the prediction for each sample indexed by i.

5fill in blank

hard

Fill all three blanks to create a dictionary of support vectors and their indices.

ML Python

support_info = {idx: vec for idx, vec in enumerate(model.[1]) if idx [2] len(model.[3])}

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

Asupport_vectors_

B<

Csupport_

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'support_vectors' without underscore which is invalid.

Using '>' instead of '<' for index comparison.

support_vectors_ holds the vectors, support_ holds their indices, and we check if the index is less than the length of support_.

Practice

1. What is the main goal of a Support Vector Machine (SVM) in machine learning?

easy

A. To cluster data points into groups without labels

B. To reduce the number of features in the dataset

C. To find the best boundary that separates different classes

D. To generate random predictions for data points

Solution

1. Step 1: Understand SVM's purpose

   SVM aims to separate data points into classes by drawing a boundary.

2. Step 2: Identify the best boundary

   SVM finds the boundary (called hyperplane) that maximizes the margin between classes.

3. Final Answer:

   To find the best boundary that separates different classes -> Option C

4. Quick Check:

   SVM goal = best separating boundary [OK]

Hint: SVM separates classes with the widest margin [OK]

Common Mistakes:

• Confusing SVM with clustering methods
• Thinking SVM reduces features
• Believing SVM makes random predictions

2. Which of the following is the correct way to import the SVM classifier from scikit-learn in Python?

easy

A. import svm from sklearn

B. from sklearn.svm import SVC

C. from sklearn import svm.SVC

D. import SVC from sklearn.svm

Solution

1. Step 1: Recall scikit-learn import syntax

   Correct import uses 'from module import class' format.

2. Step 2: Match correct syntax for SVM

   The SVM classifier is called SVC and is imported from sklearn.svm as 'from sklearn.svm import SVC'.

3. Final Answer:

   from sklearn.svm import SVC -> Option B

4. Quick Check:

   Correct import = from sklearn.svm import SVC [OK]

Hint: Use 'from sklearn.svm import SVC' to import SVM [OK]

Common Mistakes:

• Using wrong import order
• Trying to import class directly without module
• Using invalid syntax like 'import svm from sklearn'

3. What will be the output of the following Python code using SVM?

from sklearn.svm import SVC
import numpy as np

X = np.array([[0, 0], [1, 1]])
y = [0, 1]
model = SVC(kernel='linear')
model.fit(X, y)
pred = model.predict([[2, 2]])
print(pred)

medium

A. [0]

B. Error

C. [2]

D. [1]

Solution

1. Step 1: Understand training data and labels

   Training points: [0,0] labeled 0 and [1,1] labeled 1.

2. Step 2: Predict label for [2,2]

   [2,2] is closer to class 1 side since it lies beyond [1,1]. So prediction is 1.

3. Final Answer:

   [1] -> Option D

4. Quick Check:

   Point [2,2] predicted class = 1 [OK]

Hint: Points near class 1 get label 1 [OK]

Common Mistakes:

• Predicting label 0 for point far from class 0
• Expecting error due to small data
• Confusing kernel effect on prediction

4. Identify the error in this SVM code snippet:

from sklearn.svm import SVC
X = [[1, 2], [2, 3], [3, 4]]
y = [0, 1]
model = SVC()
model.fit(X, y)

medium

A. Mismatch in number of samples and labels

B. Wrong import statement

C. Kernel parameter missing

D. Predict method not called

Solution

1. Step 1: Check data and label lengths

   X has 3 samples but y has only 2 labels, causing mismatch.

2. Step 2: Understand fit requirements

   fit() requires equal number of samples and labels to train properly.

3. Final Answer:

   Mismatch in number of samples and labels -> Option A

4. Quick Check:

   Samples count != labels count causes error [OK]

Hint: Samples and labels must match in length [OK]

Common Mistakes:

• Ignoring length mismatch
• Thinking kernel is mandatory
• Expecting error without calling predict

5. You have a dataset with two classes that are not linearly separable. Which SVM kernel should you choose to improve classification?

hard

A. RBF (Radial Basis Function) kernel

B. Polynomial kernel

C. Linear kernel

D. Sigmoid kernel

Solution

1. Step 1: Understand data separability

   Non-linearly separable data means a straight line can't separate classes well.

2. Step 2: Choose kernel for complex boundaries

   RBF kernel maps data into higher dimension to separate complex shapes effectively.

3. Final Answer:

   RBF (Radial Basis Function) kernel -> Option A

4. Quick Check:

   Non-linear data needs RBF kernel [OK]

Hint: Use RBF kernel for non-linear data [OK]

Common Mistakes:

• Choosing linear kernel for non-linear data
• Confusing polynomial and RBF kernels
• Ignoring kernel choice impact