Data Science: Machine Learning

What is the main goal of machine learning?

• To prevent computers from learning

• To make computers more expensive

• To decrease the amount of data available

• To enable computers to learn from data and improve performance on a specific task.

What are the two main types of machine learning?

• reinforcement learning

• semi-supervised learning

• supervised learning and unsupervised learning

• deep learning

Explain the difference between supervised and unsupervised learning.

• In supervised learning, the model is trained on labeled data, while in unsupervised learning, the model is trained on unlabeled data.

• In supervised learning, the model is trained on unlabeled data, while in unsupervised learning, the model is trained on labeled data.

• Supervised learning uses neural networks, while unsupervised learning uses decision trees.

• Supervised learning is used for classification tasks only, while unsupervised learning is used for regression tasks only.

What is overfitting in machine learning?

• Overfitting is when a model learns the details and noise in the training data to the extent that it negatively impacts the model's performance on new data.

• Overfitting occurs when a model is too simple and cannot capture the underlying patterns in the data.

• Overfitting is when a model learns only the general patterns in the training data.

• Overfitting is when a model performs well on new data but poorly on the training data.

What is the purpose of a confusion matrix in machine learning?

• To calculate the mean of the dataset

• To evaluate the performance of a classification model.

• To determine the optimal learning rate

• To visualize the decision boundary

What is the role of feature selection in machine learning?

• Feature selection has no impact on the accuracy of machine learning models

• Feature selection helps in selecting the most important features that contribute to the prediction task while ignoring irrelevant or redundant ones.

• Feature selection only focuses on adding more features to improve model performance

• Feature selection randomly picks features without considering their relevance

What is the difference between classification and regression in machine learning?

• Classification is for categorical output, regression is for continuous output.

• Classification is used for regression tasks, regression is used for classification tasks.

• Classification and regression are the same concept in machine learning.

• Classification is for continuous output, regression is for categorical output.

What is the bias-variance tradeoff in machine learning?

• The bias-variance tradeoff is the compromise between supervised and unsupervised learning techniques.

• The bias-variance tradeoff refers to the balance between precision and recall in machine learning.

• The bias-variance tradeoff is the conflict between linear and non-linear regression models.

• The bias-variance tradeoff in machine learning refers to the tradeoff between the error introduced by bias (underfitting) and the error introduced by variance (overfitting).

What is the importance of cross-validation in machine learning?

• Cross-validation is only used for hyperparameter tuning

• Cross-validation can only be applied to small datasets

• Cross-validation is not necessary for evaluating model performance

• Cross-validation helps in evaluating the model's generalization ability and robustness by splitting the data into multiple subsets for training and testing.

Explain the concept of ensemble learning in machine learning.

• Ensemble learning does not combine multiple models to improve performance.

• Ensemble learning involves using only one type of algorithm for prediction.

• Ensemble learning is a single model approach in machine learning.

• Ensemble learning combines multiple models to enhance predictive performance.