Mastering Machine Learning Concepts

What is overfitting in machine learning?

• 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.

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

How can you identify underfitting in a model?

• A model is underfitting if it has low training error and high validation error.

• A model is underfitting if it has high training and validation errors, indicating poor performance on both datasets.

• A model is underfitting if it performs well on the training set but poorly on the validation set.

• A model is underfitting if it has low errors on both training and validation datasets.

What is the primary goal of supervised learning?

• To reduce the dimensionality of input features.

• To cluster similar data points together.

• To predict future outcomes without labeled data.

• To learn a mapping from input features to output labels.

Which of the following is an example of unsupervised learning?

• Classification

• Regression

• Clustering

• Neural Networks

What techniques can be used to prevent overfitting?

• Techniques to prevent overfitting include regularization, cross-validation, dropout, early stopping, and data augmentation.

• Using a smaller dataset

• Increasing the learning rate

• Ignoring validation data

In supervised learning, what are the two main components of the dataset?

• Inputs and outputs

• Features and labels

• Variables and constants

• Attributes and targets

What is the difference between classification and regression in supervised learning?

• Classification predicts future values; regression predicts categories.

• Classification uses numerical data; regression uses text data.

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

• Classification deals with categorical outcomes; regression deals with continuous outcomes.

Can unsupervised learning be used for clustering? Why or why not?

• No, clustering is a form of supervised learning.

• No, unsupervised learning is only for supervised tasks.

• Yes, clustering requires labeled data.

• Yes, unsupervised learning can be used for clustering.

What role does cross-validation play in assessing model performance?

• Cross-validation is used to increase the size of the training dataset.

• Cross-validation only measures the training accuracy of a model.

• Cross-validation helps assess model performance by providing a more reliable estimate of its accuracy and generalization ability.

• Cross-validation is a technique for feature selection in machine learning.

How does the bias-variance tradeoff relate to overfitting and underfitting?

• The bias-variance tradeoff relates to overfitting and underfitting by balancing model complexity to minimize both types of errors.

• Overfitting occurs when bias is minimized and variance is maximized.

• The bias-variance tradeoff only affects underfitting, not overfitting.

• The bias-variance tradeoff is unrelated to model complexity.