Exploring Machine Learning Concepts

What is the primary goal of classification in machine learning?

• To cluster similar data points together.

• To assign labels to input data based on learned patterns.

• To predict future outcomes based on historical data.

• To reduce the dimensionality of input features.

How does regression differ from classification?

• Regression requires labeled data; classification does not require any data.

• Regression predicts continuous outcomes; classification predicts categorical outcomes.

• Regression is used for time series analysis; classification is used for clustering.

• Regression can only handle binary outcomes; classification can handle multiple outcomes.

What is a neural network and how does it function?

• A neural network is a type of computer hardware used for gaming.

• A neural network is a simple algorithm that only performs addition.

• A neural network is a model that simulates the way human brains process information, consisting of layers of interconnected nodes that learn from data.

• A neural network is a physical brain implant that enhances memory.

What are the main types of machine learning?

• Deep learning

• Semi-supervised learning

• Supervised learning, Unsupervised learning, Reinforcement learning

• Transfer learning

How do you evaluate the performance of a classification model?

• Evaluate based on the number of features used in the model.

• Use only the training accuracy to assess performance.

• Use metrics like accuracy, precision, recall, F1 score, and confusion matrix.

• Focus solely on the model's runtime efficiency.

What is a confusion matrix?

• A confusion matrix is a type of neural network architecture.

• A confusion matrix is a method for data normalization.

• A confusion matrix is a table that displays the performance of a classification model by comparing predicted and actual classifications.

• A confusion matrix is a statistical test for hypothesis testing.

What is the difference between a decision tree and a neural network?

• A decision tree is a type of neural network.

• A decision tree uses a hierarchical structure for decision-making, while a neural network uses interconnected layers to learn complex patterns.

• Neural networks are always more accurate than decision trees.

• Decision trees require more data than neural networks.