Exploring Machine Learning Concepts

What is the primary goal of supervised learning?

• To learn a mapping from input features to output labels based on labeled training data.

• To predict future outcomes without any training data.

• To optimize the performance of unsupervised learning algorithms.

• To classify data into predefined categories without labels.

How does unsupervised learning differ from supervised learning?

• Unsupervised learning requires labeled data, while supervised learning does not.

• Both unsupervised and supervised learning use the same type of data.

• Unsupervised learning is only applicable to classification tasks.

• Unsupervised learning does not use labeled data, while supervised learning does.

What is a neural network and how does it function?

• A neural network is a computational model that processes information through interconnected layers of nodes, learning by adjusting weights based on prediction errors.

• A neural network is a type of biological brain that functions without any training.

• A neural network is a hardware device that processes data without any learning capabilities.

• A neural network is a simple algorithm that only uses linear equations to make predictions.

Explain the concept of a decision tree in machine learning.

• A decision tree is a type of neural network that processes data in layers.

• A decision tree is a model that uses a tree-like structure to make decisions based on input features, splitting data into subsets to predict outcomes.

• A decision tree is a statistical method that calculates probabilities without any data splitting.

• A decision tree is a linear model that predicts outcomes based on a single feature.

What are support vector machines used for?

• Support vector machines are used for image editing.

• Support vector machines are used for classification and regression tasks.

• Support vector machines are primarily for data storage.

• Support vector machines are used for web development.

Describe the process of model evaluation in machine learning.

• The process of model evaluation in machine learning includes splitting the dataset, training the model, making predictions, and comparing results using evaluation metrics.

• Visualizing the training data

• Deploying the model to production

• Collecting data from various sources

What does ANN stand for and what is its significance?

• Automated Neural Network

• Artificial Network Node

• Artificial Neural Network

• Analog Neural Network

How do convolutional neural networks (CNN) differ from traditional neural networks?

• CNNs are only used for image processing tasks.

• Traditional neural networks are faster than CNNs for all applications.

• CNNs do not require any training data unlike traditional neural networks.

• CNNs use convolutional layers for feature extraction, while traditional neural networks use fully connected layers.

What is the KNN algorithm and how does it work?

• KNN is a non-parametric algorithm that classifies data points based on the majority class of their k nearest neighbors.

• KNN uses a decision tree to classify data points based on their features.

• KNN is a linear regression algorithm that predicts continuous values.

• KNN is a clustering algorithm that groups data points into distinct clusters.

What are the advantages of using decision trees?

• High computational cost

• Limited to binary outcomes

• Requires extensive data cleaning

• Advantages of using decision trees include interpretability, versatility with data types, minimal preprocessing, ability to model complex relationships, and applicability to both classification and regression.