BUSINESS 13 - Machine Learning Practice Quiz

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Jun 24, 2024

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Title: Machine Learning Practice Quiz Introduction: Machine Learning is a rapidly growing field in computer science that focuses on developing algorithms and statistical models that enable computers to learn and make decisions from data without being explicitly programmed. Testing your knowledge through practice quizzes is a great way to reinforce your understanding of key concepts in Machine Learning. This quiz includes 15 multiple-choice questions to assess your knowledge and test your skills. Each question has multiple answer choices, and the correct answers are provided at the end. Instruction: Choose the best answer for each of the following questions based on your knowledge of Machine Learning concepts. At the end of the quiz, you can check your answers to see how well you did. Quiz Questions: 1. Which of the following is a supervised learning algorithm? A) K-means clustering B) Decision tree C) Apriori D) AdaBoost Answer: B) Decision tree 2. What is the purpose of the activation function in a neural network? A) Normalize the input data B) Compute the dot product of input and weights C) Introduce non-linearity D) Reduce the dimensionality of the data Answer: C) Introduce non-linearity 3. Which evaluation metric is commonly used for regression tasks in Machine Learning? A) Precision B) F1-score C) Mean Absolute Error D) Recall Answer: C) Mean Absolute Error 4. What does the term "overfitting" refer to in Machine Learning? A) Model performs well on training data but poorly on unseen data B) Model performs poorly on training data and unseen data C) Model captures noise in the training data D) Model is under-trained

Answer: C) Model captures noise in the training data 5. Which algorithm is used for dimensionality reduction in Machine Learning? A) K-nearest neighbors B) Random Forest C) Principal Component Analysis (PCA) D) Support Vector Machines (SVM) Answer: C) Principal Component Analysis (PCA) 6. What is the objective of unsupervised learning? A) Predict a target variable B) Learn from labeled data C) Discover patterns and relationships in data D) Classify input data into categories Answer: C) Discover patterns and relationships in data 7. Which method is used for handling imbalanced data in Machine Learning? A) Upsampling B) Lasso regression C) Gradient descent D) Stochastic gradient boosting Answer: A) Upsampling 8. Which technique is used for feature scaling in Machine Learning? A) One-hot encoding B) Standardization C) Ensemble learning D) ReLU activation Answer: B) Standardization 9. What is the purpose of the bias term in a neural network? A) Regularize the model B) Initialze weights C) Shift the activation function D) Prevent underfitting Answer: C) Shift the activation function 10. Which algorithm is a type of ensemble learning method? A) k-means clustering B) Gradient Boosting C) Linear Regression D) Naive Bayes Answer: B) Gradient Boosting

11. Which optimization algorithm is commonly used to train neural networks? A) AdaGrad B) Bayes Optimization C) K-means D) Linear Regression Answer: A) AdaGrad 12. Which technique is used for handling missing data in a dataset? A) Mean imputation B) Ridge regression C) ROC curve analysis D) t-SNE Answer: A) Mean imputation 13. What is the purpose of the learning rate in gradient descent optimization? A) Control the speed of convergence B) Regularize the model C) Determine the number of iterations D) Adjust the bias term Answer: A) Control the speed of convergence 14. Which method is used for hyperparameter tuning in Machine Learning models? A) Grid search B) Silhouette analysis C) Entropy calculation D) Ridge regression Answer: A) Grid search 15. Which evaluation metric is commonly used for classification tasks in Machine Learning? A) R2 score B) Mean Squared Error (MSE) C) Confusion Matrix D) Root Mean Squared Error (RMSE) Answer: C) Confusion Matrix Conclusion: Practicing with quiz questions like these can help reinforce your understanding of key concepts in Machine Learning. By testing your knowledge and skills, you can identify areas that may require further study and improve your proficiency in the field. Stay curious and keep exploring the exciting world of Machine Learning! References:

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1. Mitchell, T. M. (1997). Machine learning. McGraw Hill. 2. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. Springer Science & Business Media. 3. Bishop, C. M. (2006). Pattern recognition and machine learning. Springer. 4. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. 5. Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT Press.

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