K-Nearest Neighbors (KNN) in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

How K affects KNN predictions

In K-Nearest Neighbors, what happens if you increase the value of K too much?

A The model becomes too complex and fits noise, causing overfitting.

B The model ignores all neighbors and predicts randomly.

C The model always predicts the class of the closest neighbor only.

D The model becomes too simple and may miss important patterns, causing underfitting.

💡 Hint

Think about how using many neighbors affects the decision boundary.

Increasing K means the model looks at many neighbors, smoothing predictions and possibly missing small but important details, leading to underfitting.

❓ Predict Output

intermediate

Output of KNN prediction with given neighbors

Given the following distances and labels of neighbors, what will the KNN predict for K=3?

neighbors = [(0.5, 'A'), (0.7, 'B'), (0.9, 'A'), (1.2, 'B')]

A 'C'

B 'B'

C 'A'

D Error due to invalid neighbors

💡 Hint

Count the labels of the 3 closest neighbors.

The 3 closest neighbors have labels 'A', 'B', and 'A'. 'A' appears twice, so the prediction is 'A'.

❓ Hyperparameter

advanced

Choosing the best K value

You have a dataset and want to choose the best K for KNN. Which method is the most reliable to find the best K?

A Use the training data accuracy for each K and pick the highest.

B Use cross-validation to test different K values and pick the one with best average validation accuracy.

C Pick the smallest K to avoid overfitting.

D Pick the largest K to avoid underfitting.

💡 Hint

Think about how to test model performance fairly on unseen data.

Cross-validation tests the model on different splits of data, giving a better estimate of performance and helping choose the best K.

❓ Metrics

advanced

Effect of K on bias and variance

How does increasing K in KNN affect bias and variance?

A Increasing K increases bias and decreases variance.

B Increasing K decreases bias and increases variance.

C Increasing K increases both bias and variance.

D Increasing K decreases both bias and variance.

💡 Hint

Think about how smoothing predictions affects errors.

Higher K smooths predictions, making the model simpler (higher bias) but more stable (lower variance).

🔧 Debug

expert

Identifying error in KNN distance calculation

What error does the following code produce when calculating Euclidean distance between two points?

def euclidean_distance(p1, p2):
    return sum((x - y) ** 2 for x, y in zip(p1)) ** 0.5

print(euclidean_distance([1, 2], [4, 6]))

A ValueError: not enough values to unpack (expected 2, got 1)

B TypeError: unsupported operand type(s) for -: 'int' and 'list'

C NameError: name 'y' is not defined

D 0.0 (incorrect distance)

💡 Hint

Check how zip is used in the code.

zip(p1) produces single-element tuples, but the code tries to unpack into two variables x and y, causing ValueError: not enough values to unpack (expected 2, got 1).

Practice

1. What does the K in K-Nearest Neighbors (KNN) represent?

easy

A. The number of closest data points used to predict the label

B. The number of features in the dataset

C. The number of classes in the target variable

D. The number of training samples

Solution

1. Step 1: Understand the role of K in KNN

   K represents how many nearest neighbors the algorithm looks at to decide the label for a new point.

2. Step 2: Differentiate K from other terms

   K is not about features, classes, or total samples but specifically the count of neighbors considered.

3. Final Answer:

   The number of closest data points used to predict the label -> Option A

4. Quick Check:

   K = number of neighbors [OK]

Hint: K means neighbors counted to decide label [OK]

Common Mistakes:

• Confusing K with number of features
• Thinking K is number of classes
• Assuming K is total data points

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

easy

A. import sklearn.neighbors.KNeighborsClassifier

B. import KNeighborsClassifier from sklearn.neighbors

C. from sklearn import KNeighborsClassifier

D. from sklearn.neighbors import KNeighborsClassifier

Solution

1. Step 1: Recall correct Python import syntax

   In Python, the correct way to import a class from a module is 'from module import Class'.

2. Step 2: Match with scikit-learn KNN import

   The KNeighborsClassifier is inside sklearn.neighbors, so the correct import is 'from sklearn.neighbors import KNeighborsClassifier'.

3. Final Answer:

   from sklearn.neighbors import KNeighborsClassifier -> Option D

4. Quick Check:

   Correct import uses 'from ... import ...' [OK]

Hint: Use 'from module import Class' syntax [OK]

Common Mistakes:

• Using 'import Class from module' which is invalid
• Trying to import directly from sklearn without neighbors
• Using dot notation in import statement

3. What will be the predicted label for the point (3, 3) using KNN with k=3 and the following training data?

X = [[1, 2], [2, 3], [3, 4], [6, 7]]
y = ['A', 'A', 'B', 'B']

medium

A. 'B'

B. 'A'

C. 'C'

D. Error due to missing label

Solution

1. Step 1: Calculate distances from (3,3) to each training point

   Distances: to (1,2) ≈ 2.24, to (2,3) = 1, to (3,4) = 1, to (6,7) ≈ 5

2. Step 2: Find 3 nearest neighbors and their labels

   Nearest neighbors: (2,3) 'A', (3,4) 'B', (1,2) 'A'. Labels are ['A', 'B', 'A'].

3. Step 3: Determine majority label

   Label 'A' appears twice, 'B' once, so predicted label is 'A'.

4. Final Answer:

   'A' -> Option B

5. Quick Check:

   Majority vote among 3 neighbors = 'A' [OK]

Hint: Pick majority label among closest 3 points [OK]

Common Mistakes:

• Ignoring distance and picking random label
• Choosing label of closest single neighbor only
• Miscounting neighbors or labels

4. Identify the error in this KNN code snippet:

from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)
preds = knn.predict(X_test)
print(preds)

Assuming X_train and y_train are numpy arrays but y_train contains string labels.

medium

A. Missing data scaling causes error

B. y_train must be numeric, strings cause error

C. No error, code runs fine with string labels

D. n_neighbors must be less than number of samples

Solution

1. Step 1: Check if KNN supports string labels

   KNN in scikit-learn supports string labels for classification without error.

2. Step 2: Verify other possible errors

   Data scaling is recommended but not mandatory; n_neighbors=3 is valid if training samples ≥3.

3. Final Answer:

   No error, code runs fine with string labels -> Option C

4. Quick Check:

   KNN accepts string labels directly [OK]

Hint: KNN accepts string labels; no numeric conversion needed [OK]

Common Mistakes:

• Assuming string labels cause errors
• Confusing scaling as mandatory
• Ignoring sample size for n_neighbors

5. You have a dataset with features on very different scales (e.g., age 0-100, income 0-100000). Why is it important to scale features before using KNN, and what could happen if you don't?

hard

A. Scaling ensures all features contribute equally; without it, large-scale features dominate distance calculations

B. Scaling is not needed; KNN automatically adjusts for feature scales

C. Scaling reduces training time but does not affect predictions

D. Scaling changes labels to numeric values for KNN

Solution

1. Step 1: Understand distance calculation in KNN

   KNN uses distance (like Euclidean) between points; features with larger scales affect distance more.

2. Step 2: Explain effect of unscaled features

   If features are not scaled, large-scale features (like income) dominate, making smaller-scale features (like age) ignored.

3. Step 3: Importance of scaling

   Scaling puts all features on similar scale so each contributes fairly to distance and prediction.

4. Final Answer:

   Scaling ensures all features contribute equally; without it, large-scale features dominate distance calculations -> Option A

5. Quick Check:

   Scale features to balance distances [OK]

Hint: Scale features so no one dominates distance [OK]

Common Mistakes:

• Thinking KNN auto-scales features
• Believing scaling changes labels
• Assuming scaling only affects speed