Understanding Machine Learning Concepts

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 machine learning approach that uses labeled data to train models.

• Supervised learning is a type of reinforcement learning.

Define unsupervised learning.

• Unsupervised learning requires labeled data for training.

• Unsupervised learning is a method for supervised classification tasks.

• Unsupervised learning only works with structured data.

• Unsupervised learning is a machine learning approach that analyzes and identifies patterns in unlabeled data.

What is overfitting in machine learning?

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

• 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.

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

Explain the concept of a confusion matrix.

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

• A confusion matrix is a tool used for data preprocessing in machine learning.

• A confusion matrix is a graph that displays the accuracy of a regression model.

• A confusion matrix is a table that summarizes the performance of a classification model by showing true positives, true negatives, false positives, and false negatives.

What is the purpose of cross-validation?

• To optimize the model's hyperparameters.

• To increase the size of the training dataset.

• To reduce the complexity of the model.

• The purpose of cross-validation is to evaluate the performance and generalizability of a model.

Describe the difference between classification and regression.

• Classification predicts numerical values; regression predicts categories.

• Classification predicts categories; regression predicts continuous values.

• Classification requires labeled data; regression does not need any data.

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

What is a neural network?

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

• A neural network is a biological structure found in animals.

• A neural network is a software application for word processing.

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

What role does feature selection play in model performance?

• Feature selection only affects the training time of the model.

• Feature selection decreases model accuracy and increases complexity.

• Feature selection has no impact on model performance.

• Feature selection improves model performance by enhancing accuracy and reducing complexity.