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Machine Learning Logistic Regression

This quiz is designed to assess your understanding of Logistic Regression, a widely used algorithm in Machine Learning for binary classification tasks.

15 Questions Published
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Questions

Question 1 Multiple Choice (Single Answer)

Logistic Regression is a type of:

  1. Linear Regression
  2. Polynomial Regression
  3. Classification Algorithm
  4. Clustering Algorithm
Question 2 Multiple Choice (Single Answer)

The output of Logistic Regression is:

  1. A continuous value between 0 and 1
  2. A binary value (0 or 1)
  3. A probability value between 0 and 1
  4. A vector of coefficients
Question 3 Multiple Choice (Single Answer)

The sigmoid function used in Logistic Regression is given by:

  1. $$f(x) = x^2$$
  2. $$f(x) = e^x$$
  3. $$f(x) = 1 / (1 + e^{-x})$$
  4. $$f(x) = x$$
Question 4 Multiple Choice (Single Answer)

The cost function used in Logistic Regression is:

  1. Mean Squared Error (MSE)
  2. Root Mean Squared Error (RMSE)
  3. Cross-Entropy Loss
  4. L1 Regularization
Question 5 Multiple Choice (Single Answer)

Which regularization technique is commonly used in Logistic Regression to prevent overfitting?

  1. L1 Regularization
  2. L2 Regularization
  3. Dropout
  4. Early Stopping
Question 6 Multiple Choice (Single Answer)

Logistic Regression is particularly useful when:

  1. The data is linearly separable
  2. The data has a normal distribution
  3. The data is highly imbalanced
  4. The data is categorical
Question 7 Multiple Choice (Single Answer)

Which of the following is NOT a hyperparameter in Logistic Regression?

  1. Learning Rate
  2. Regularization Parameter
  3. Number of Epochs
  4. Activation Function
Question 8 Multiple Choice (Single Answer)

How can you improve the performance of Logistic Regression when the data is imbalanced?

  1. Use a different classification algorithm
  2. Resample the data
  3. Use a cost-sensitive learning algorithm
  4. Increase the regularization parameter
Question 9 Multiple Choice (Single Answer)

What is the main advantage of Logistic Regression over other classification algorithms?

  1. It is more interpretable
  2. It is more accurate
  3. It is faster to train
  4. It requires less data
Question 10 Multiple Choice (Single Answer)

Which of the following is NOT a common evaluation metric for Logistic Regression?

  1. Accuracy
  2. Precision
  3. Recall
  4. F1 Score
Question 11 Multiple Choice (Single Answer)

Logistic Regression can be extended to handle multi-class classification problems using:

  1. One-vs-All
  2. One-vs-One
  3. Error-Correcting Output Codes
  4. Softmax Regression
Question 12 Multiple Choice (Single Answer)

Which of the following is NOT a common application of Logistic Regression?

  1. Predicting customer churn
  2. Detecting fraudulent transactions
  3. Image classification
  4. Natural language processing
Question 13 Multiple Choice (Single Answer)

Logistic Regression is particularly sensitive to:

  1. Outliers
  2. Missing values
  3. Feature scaling
  4. Data imbalance
Question 14 Multiple Choice (Single Answer)

To improve the stability and robustness of Logistic Regression, one can:

  1. Use a larger dataset
  2. Regularize the model
  3. Use a different classification algorithm
  4. Increase the number of features
Question 15 Multiple Choice (Single Answer)

Which of the following is NOT a disadvantage of Logistic Regression?

  1. It can suffer from overfitting
  2. It is sensitive to outliers
  3. It is computationally expensive
  4. It requires a large amount of data
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