K-Nearest Neighbors (KNN) in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the KNeighborsClassifier from scikit-learn.

ML Python

from sklearn.neighbors import [1]

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

AKNeighborsClassifier

BRandomForestClassifier

CLinearRegression

DSVC

Attempts:

3 left

💡 Hint

Common Mistakes

Importing a different model like RandomForestClassifier or SVC.

Using regression classes instead of classification.

The KNeighborsClassifier is the class used to create a KNN model in scikit-learn.

2fill in blank

medium

Complete the code to create a KNN model with 3 neighbors.

ML Python

model = KNeighborsClassifier(n_neighbors=[1])

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

A10

B5

C1

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the default 5 neighbors instead of 3.

Choosing a number too large or too small without reason.

Setting n_neighbors=3 creates a KNN model that looks at the 3 closest points to decide the class.

3fill in blank

hard

Fix the error in the code to fit the KNN model on training data X_train and y_train.

ML Python

model.[1](X_train, y_train)

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

Atransform

Bscore

Cfit

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict before training the model.

Trying to transform data with KNN which is not a transformer.

The fit method trains the model using the training data and labels.

4fill in blank

hard

Fill both blanks to predict labels for X_test and calculate accuracy score.

ML Python

predictions = model.[1](X_test)
accuracy = accuracy_score(y_test, [2])

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

Apredict

Bpredictions

Cfit

Dtransform

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit instead of predict for predictions.

Passing the model or data instead of predictions to accuracy_score.

First, use predict to get predictions. Then pass those predictions to accuracy_score to get accuracy.

5fill in blank

hard

Fill all three blanks to create a dictionary of predictions for each test sample with their index, predicted label, and distance to neighbors.

ML Python

results = {i: {'label': [1][i], 'distances': [2][i]} for i in range(len([3]))}

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

Apredictions

Bdistances

CX_test

Dy_test

Attempts:

3 left

💡 Hint

Common Mistakes

Using true labels y_test instead of predicted labels.

Mixing up distances with labels or data.

This dictionary comprehension loops over test samples. For each index i, it stores the predicted label and distances to neighbors from the test data.

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