Machine Learning Basics

What is the primary goal of machine learning?

• To replace human decision-making entirely

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

• To make computers faster at processing data

• To create self-aware artificial intelligence

What are the two main types of machine learning?

• deep learning

• semi-supervised learning

• reinforcement learning

• supervised learning and unsupervised learning

Explain the difference between supervised and unsupervised learning.

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

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

• Unsupervised learning is more accurate than supervised learning.

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

What is overfitting in machine learning?

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

• Overfitting in machine learning 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 is when a model performs well on new data but poorly on training data

• Overfitting is the process of removing noise from the training data to improve model performance

What is the purpose of a validation set in machine learning?

• The purpose of a validation set in machine learning is to evaluate the model's performance and prevent overfitting.

• To introduce bias into the model

• To confuse the model during training

• To increase the accuracy of the model

What is the difference between classification and regression?

• Classification predicts categories, regression predicts continuous values.

• Classification predicts continuous values, regression predicts categories.

• Classification and regression are the same thing.

• Classification is used for continuous data, regression is used for categorical data.

What is a neural network and how does it work?

• A neural network is a form of biological communication

• A neural network works by storing data in a single node

• A neural network is a series of algorithms that recognize patterns. It works by passing input data through interconnected nodes to produce an output.

• A neural network is a type of computer hardware

What is the role of activation functions in neural networks?

• Activation functions introduce non-linearity to the neural network, allowing it to learn complex patterns and relationships in the data.

• Activation functions are only used in the output layer of neural networks

• Activation functions reduce the complexity of neural networks

• Activation functions slow down the learning process in neural networks

What is the bias-variance tradeoff in machine learning?

• The bias-variance tradeoff in machine learning is the balance between a model's ability to capture the underlying patterns in the training data (bias) and its ability to generalize to new, unseen data (variance).

• The bias-variance tradeoff is the balance between the number of features and the model's performance.

• The bias-variance tradeoff is the tradeoff between model complexity and interpretability.

• The bias-variance tradeoff refers to the tradeoff between precision and recall in classification models.

How can you evaluate the performance of a machine learning model?

• Use metrics like accuracy, precision, recall, F1 score, and ROC-AUC.

• Ask a magic eight ball for the model evaluation.

• Flip a coin to determine the model's performance.

• Use metrics like happiness, sadness, anger, and fear.