Exploring Machine Learning Concepts

What is machine learning?

• Machine learning is a method of data analysis that automates analytical model building.

• Machine learning is a type of computer hardware.

• Machine learning is a programming language.

• Machine learning is a form of data storage.

Define supervised learning.

• Supervised learning is a method that requires no data for training.

• Supervised learning is a type of reinforcement learning.

• Unsupervised learning uses labeled data to train models.

• Supervised learning is a machine learning approach that uses labeled data to train models to predict outcomes.

What is unsupervised learning?

• A technique used only for classification tasks.

• Unsupervised learning is a machine learning approach that identifies patterns in data without labeled outcomes.

• An approach that predicts future outcomes based on past data.

• A method that requires labeled data for training.

Explain reinforcement learning.

• Reinforcement learning is a technique for clustering data points into groups.

• Reinforcement learning involves training a model on static datasets without interaction.

• Reinforcement learning is a learning paradigm where an agent learns to make decisions by interacting with an environment to maximize cumulative rewards.

• Reinforcement learning is a method for supervised learning using labeled data.

What are the main steps in the ML workflow?

• Algorithm Optimization

• Data Visualization

• 1. Problem Definition, 2. Data Collection, 3. Data Preprocessing, 4. Feature Engineering, 5. Model Selection, 6. Model Training, 7. Model Evaluation, 8. Model Deployment, 9. Monitoring and Maintenance.

• Data Storage

What is regression in machine learning?

• Regression is a method for reducing the dimensionality of data.

• Regression is a supervised learning technique used to predict continuous outcomes.

• Regression is an unsupervised learning technique for clustering data.

• Regression is used to classify categorical outcomes.

How does classification differ from regression?

• Classification predicts numerical values; regression predicts categories.

• Classification is used for time series; regression is for image analysis.

• Classification predicts categories; regression predicts continuous values.

• Classification requires more data than regression.

What is overfitting in a machine learning model?

• Overfitting is when a model performs well on training data but poorly on unseen data due to excessive complexity.

• Overfitting is when a model performs poorly on both training and unseen data due to lack of data.

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

• Overfitting happens when a model is trained on too much data, leading to confusion.

What is the purpose of a training dataset?

• To store the final model parameters.

• To validate a machine learning model's performance.

• The purpose of a training dataset is to train a machine learning model.

• To generate random data for testing purposes.

What role does a validation dataset play?

• The validation dataset is only for testing the model's final performance.

• The validation dataset is used for training the model.

• The validation dataset is irrelevant to model development.

• The validation dataset helps in tuning model parameters and preventing overfitting.