Machine Learning Basics

What is machine learning?

• Machine learning is a type of computer hardware.

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

• Machine learning is a programming language for data analysis.

• Machine learning is a method for manual data entry.

What are the main types of machine learning?

• Supervised learning, Unsupervised learning, Reinforcement learning

• Transfer learning

• Semi-supervised learning

• Deep learning

What is supervised learning?

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

• Unsupervised learning uses labeled data to train models.

• Supervised learning is a type of reinforcement learning.

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

What is unsupervised learning?

• Unsupervised learning is a type of supervised learning.

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

• Unsupervised learning requires labeled data to function effectively.

• Unsupervised learning is only applicable to classification tasks.

What is reinforcement learning?

• Reinforcement learning is a type of machine learning focused on training agents to make decisions through trial and error to maximize rewards.

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

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

• Reinforcement learning is a type of machine learning that focuses solely on data analysis.

What is overfitting in machine learning?

• Overfitting is when a model performs equally well on both training and unseen data.

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

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

• Overfitting refers to the process of reducing the complexity of a model to improve its performance.

What is underfitting in machine learning?

• Underfitting happens when a model has too many parameters.

• Underfitting is when a model is too simple to learn the underlying structure of the data.

• Underfitting occurs when a model is too complex for the data.

• Underfitting is when a model perfectly fits the training data.

What is a training set?

• A training set is a dataset used for testing a model.

• A training set is a collection of user feedback.

• A training set is a type of machine learning algorithm.

• A training set is a dataset used to train a machine learning model.

What is a test set?

• A test set is a collection of algorithms used to improve model accuracy.

• A test set is a subset of data used to evaluate the performance of a machine learning model.

• A test set is a random selection of features from the dataset.

• A test set is the entire dataset used for training a model.

What is cross-validation?

• A method for increasing the size of a dataset.

• Cross-validation is a technique for assessing how the results of a statistical analysis will generalize to an independent data set.

• A way to eliminate outliers from a dataset.

• A technique for visualizing data distributions.