Machine Learning Quiz

What is supervised learning?

• Supervised learning is a type of machine learning where an algorithm learns from unlabeled training data to make predictions or decisions.

• Supervised learning is a type of machine learning where an algorithm learns from both labeled and unlabeled training data to make predictions or decisions.

• Supervised learning is a type of machine learning where an algorithm learns from labeled training data to make predictions or decisions.

• Supervised learning is a type of machine learning where an algorithm learns from unstructured data to make predictions or decisions.

Give an example of supervised learning.

• Image recognition

• Stock market prediction

• Social media sentiment analysis

• Email spam classification

What is unsupervised learning?

• Unsupervised learning is a type of machine learning where the algorithm learns patterns and relationships in data with labeled examples.

• Unsupervised learning is a type of machine learning where the algorithm learns patterns and relationships in data with guidance from a human.

• Unsupervised learning is a type of machine learning where the algorithm learns patterns and relationships in data by following a predefined set of rules.

• Unsupervised learning is a type of machine learning where the algorithm learns patterns and relationships in data without any labeled examples or guidance from a human.

What are some applications of unsupervised learning?

• Clustering, anomaly detection, and dimensionality reduction.

• Natural language processing, computer vision, and speech recognition.

• Regression, classification, and reinforcement learning.

• Decision trees, random forests, and support vector machines.

What are neural networks?

• Neural networks are interconnected nodes organized in layers that can learn complex patterns and make predictions.

• Neural networks are a type of programming language.

• Neural networks are used to store and retrieve data.

• Neural networks are a type of computer hardware.

How do neural networks learn?

• Backpropagation and gradient descent

• Pattern recognition and decision making.

• Random guessing and trial and error.

• Supervised learning and unsupervised learning.

What is a decision tree?

• A decision tree is a mathematical equation used for decision-making.

• A decision tree is a type of graph used for data visualization.

• A decision tree is a type of tree found in forests.

• A decision tree is a flowchart-like structure used for decision-making in machine learning and data mining.

What are the advantages of using decision trees?

• Decision trees are only applicable to classification tasks.

• Decision trees have several advantages such as easy interpretation, handling of both categorical and numerical data, requiring little data preparation, handling of missing values, providing clear visualization, and being applicable to both classification and regression tasks.

• Decision trees cannot handle missing values.

• Decision trees require extensive data preparation.

What is the difference between supervised and unsupervised learning?

• Supervised learning uses labeled data, while unsupervised learning uses unlabeled data.

• Supervised learning and unsupervised learning both use unlabeled data.

• Supervised learning and unsupervised learning both use labeled data.

• Supervised learning uses unlabeled data, while unsupervised learning uses labeled data.

How can decision trees be used for classification?

• By splitting the data based on different features and creating a tree-like structure.

• By randomly assigning labels to the data points.

• By using linear regression to fit the data.

• By ignoring the features and only considering the target variable.