Logistic regression in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding the output of logistic regression

What does the output of a logistic regression model represent?

A The probability that the input belongs to the positive class

B The exact class label predicted by the model

C The distance of the input from the decision boundary in feature space

D The sum of squared errors between predicted and actual values

💡 Hint

Think about what logistic regression outputs before applying a threshold.

Logistic regression outputs a probability value between 0 and 1 indicating the likelihood that the input belongs to the positive class. The final class label is obtained by applying a threshold (usually 0.5) to this probability.

❓ Predict Output

intermediate

Output of logistic regression prediction code

What is the output of this Python code using scikit-learn's LogisticRegression?

ML Python

from sklearn.linear_model import LogisticRegression
import numpy as np

X = np.array([[1, 2], [2, 3], [3, 4], [4, 5]])
y = np.array([0, 0, 1, 1])
model = LogisticRegression()
model.fit(X, y)
preds = model.predict(np.array([[1, 2], [3, 5]]))
print(preds.tolist())

A [1, 1]

B [1, 0]

C [0, 0]

D [0, 1]

💡 Hint

The model learns to separate classes based on the training data pattern.

The model predicts class 0 for input [1, 2] because it is similar to training samples labeled 0, and class 1 for [3, 5] because it is closer to samples labeled 1.

❓ Hyperparameter

advanced

Choosing the regularization parameter in logistic regression

Which effect does increasing the regularization strength (C parameter) in scikit-learn's LogisticRegression have?

A It decreases regularization, allowing the model to fit training data more closely

B It changes the number of iterations during training

C It changes the learning rate of the model

D It increases regularization, forcing the model to be simpler

💡 Hint

Remember that in scikit-learn, C is the inverse of regularization strength.

In scikit-learn, a higher C means less regularization, so the model can fit the training data more closely. Lower C means stronger regularization, which simplifies the model.

❓ Metrics

advanced

Interpreting logistic regression evaluation metrics

If a logistic regression model has a high accuracy but low recall on the positive class, what does this imply?

A The model performs well on both positive and negative classes

B The model correctly identifies most positive cases but misclassifies many negatives

C The model misses many positive cases but correctly identifies most negatives

D The model has overfitted the training data

💡 Hint

Recall measures how many actual positives are found by the model.

Low recall means many positive cases are missed (false negatives). High accuracy can still occur if negatives are correctly classified, especially if negatives are the majority.

🔧 Debug

expert

Identifying the cause of poor logistic regression convergence

Given this code snippet, why might the logistic regression model fail to converge?

ML Python

from sklearn.linear_model import LogisticRegression
import numpy as np

X = np.random.rand(1000, 50)
y = np.random.randint(0, 2, 1000)
model = LogisticRegression(max_iter=10)
model.fit(X, y)

A The input features X contain random values causing errors

B max_iter is too low for the model to converge on this data

C The target y has only two classes which logistic regression cannot handle

D The model requires normalization of X before fitting

💡 Hint

Consider the number of iterations allowed for training.

The default logistic regression solver may need more iterations to converge on high-dimensional data. Setting max_iter too low (10) often causes convergence warnings or failure.

Practice

1. What is the main purpose of logistic regression in machine learning?

easy

A. To cluster data points into groups

B. To predict binary outcomes like yes/no or true/false

C. To reduce the number of features in data

D. To generate random data samples

Solution

1. Step 1: Understand logistic regression goal

   Logistic regression is used to predict categories with two possible outcomes, such as yes/no or true/false.

2. Step 2: Compare with other options

   Clustering groups data (not logistic regression), feature reduction simplifies data, and random sampling is unrelated.

3. Final Answer:

   To predict binary outcomes like yes/no or true/false -> Option B

4. Quick Check:

   Logistic regression = binary prediction [OK]

Hint: Logistic regression predicts yes/no answers fast [OK]

Common Mistakes:

• Confusing logistic regression with clustering
• Thinking it reduces features
• Assuming it generates data

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

easy

A. import logistic_regression from sklearn.linear_model

B. import LogisticRegression from sklearn.linear_model

C. from sklearn import LogisticRegression

D. from sklearn.linear_model import LogisticRegression

Solution

1. Step 1: Recall correct import syntax

   In Python, the correct import uses 'from module import class' format, so 'from sklearn.linear_model import LogisticRegression' is correct.

2. Step 2: Identify incorrect syntax

   Options B, C, and D use wrong order or wrong names causing syntax errors.

3. Final Answer:

   from sklearn.linear_model import LogisticRegression -> Option D

4. Quick Check:

   Correct import = from sklearn.linear_model import LogisticRegression [OK]

Hint: Use 'from module import class' for sklearn imports [OK]

Common Mistakes:

• Using wrong import order
• Wrong class or module names
• Using lowercase instead of correct class name

3. What will be the output of this Python code snippet?

from sklearn.linear_model import LogisticRegression
import numpy as np

X = np.array([[0], [1], [2], [3]])
y = np.array([0, 0, 1, 1])
model = LogisticRegression()
model.fit(X, y)
pred = model.predict([[1.5]])
print(pred)

medium

A. [1]

B. [0]

C. [2]

D. Error

Solution

1. Step 1: Understand training data and labels

   X has values 0,1 labeled 0 and 2,3 labeled 1. The model learns to separate near 1.5 between classes 0 and 1.

2. Step 2: Predict for input 1.5

   Since 1.5 is closer to class 1 range, logistic regression predicts class 1.

3. Final Answer:

   [1] -> Option A

4. Quick Check:

   Input 1.5 predicts class 1 [OK]

Hint: Predict near boundary to guess class [OK]

Common Mistakes:

• Assuming prediction is 0 for 1.5
• Expecting error due to input shape
• Confusing output format

4. Identify the error in this logistic regression code snippet:

from sklearn.linear_model import LogisticRegression
X = [[0], [1], [2], [3]]
y = [0, 0, 1, 1]
model = LogisticRegression()
model.fit(X, y)
pred = model.predict([1.5])
print(pred)

medium

A. Input to predict should be 2D array, not 1D

B. Labels y should be strings, not integers

C. Model must be initialized with parameters

D. fit method requires numpy arrays, not lists

Solution

1. Step 1: Check input shape for prediction

   Model expects 2D array for predict input, but [1.5] is 1D. It should be [[1.5]].

2. Step 2: Verify other parts

   Labels as integers are fine, model can be default, and fit accepts lists converted internally.

3. Final Answer:

   Input to predict should be 2D array, not 1D -> Option A

4. Quick Check:

   Predict input shape = 2D array [OK]

Hint: Always use double brackets for single sample prediction [OK]

Common Mistakes:

• Passing 1D array to predict
• Thinking labels must be strings
• Assuming model needs parameters to initialize

5. You have a dataset with features that are not scaled and vary widely in range. What is the best practice before applying logistic regression to improve model performance?

hard

A. Use logistic regression without any preprocessing

B. Remove half of the features randomly

C. Scale or normalize features to similar ranges

D. Increase the number of training samples without scaling

Solution

1. Step 1: Understand feature scaling importance

   Logistic regression uses weights sensitive to feature scale; large differences can cause poor training.

2. Step 2: Choose best preprocessing

   Scaling or normalizing features to similar ranges helps the model learn better and converge faster.

3. Final Answer:

   Scale or normalize features to similar ranges -> Option C

4. Quick Check:

   Feature scaling improves logistic regression [OK]

Hint: Always scale features before logistic regression [OK]

Common Mistakes:

• Ignoring feature scaling
• Randomly removing features
• Assuming more data fixes scaling issues