Exploring Machine Learning Concepts

What is the main goal of supervised learning?

• To classify data without any labels.

• To predict future outcomes without any data.

• To generate new data points from existing data.

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

Which algorithm is commonly used in unsupervised learning?

• K-means clustering

• Decision tree

• Linear regression

• Support vector machine

What is a neural network primarily used for?

• Natural language translation

• Data storage and retrieval

• Classification and regression tasks in machine learning.

• Image processing and enhancement

How does a decision tree make predictions?

• A decision tree predicts outcomes by traversing from the root to a leaf node based on feature values.

• A decision tree relies on neural networks to determine predictions.

• A decision tree predicts outcomes based on the average of all feature values.

• A decision tree uses random sampling to make predictions.

What is overfitting in the context of machine learning?

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

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

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

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

What metric is commonly used to evaluate classification models?

• Precision

• Recall

• F1 Score

• Accuracy

What is the difference between regression and classification?

• Regression predicts continuous outcomes; classification predicts categorical outcomes.

• Regression is used for time series analysis; classification is for trend analysis.

• Regression requires labeled data; classification does not require any data.

• Regression deals with binary outcomes; classification deals with numerical outcomes.

What role does the activation function play in neural networks?

• The activation function determines the learning rate.

• The activation function is used for weight initialization.

• The activation function enables non-linearity in neural networks.

• The activation function increases the number of neurons.

How can you prevent overfitting in a model?

• Use techniques like regularization, cross-validation, and dropout.

• Increase the learning rate

• Use a smaller dataset

• Ignore validation metrics

What is cross-validation and why is it important?

• Cross-validation is a technique to visualize data distributions.

• Cross-validation is a method to increase the size of a dataset.

• Cross-validation is only used for linear regression models.

• Cross-validation is important because it helps to assess how the results of a statistical analysis will generalize to an independent dataset, reduces overfitting, and provides a more accurate measure of model performance.