Exploring Machine Learning Concepts

What is machine learning?

• Machine learning is a programming language for data analysis.

• Machine learning is a method for manual data entry.

• Machine learning is a type of computer hardware.

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

Name the two main types of machine learning.

• Active learning and Batch learning

• Deep learning and Transfer learning

• Supervised learning and Unsupervised learning

• Reinforced learning and Semi-supervised learning

What is regression in the context of machine learning?

• Regression is a method for reducing the dimensionality of data.

• Regression is an unsupervised learning method for clustering data.

• Regression is a supervised learning method for predicting continuous outcomes.

• Regression is a technique used for classifying categorical outcomes.

How does classification differ from regression?

• Classification requires more data than regression for accuracy.

• Classification predicts categories; regression predicts continuous values.

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

• Classification predicts numerical values; regression predicts categories.

What is the Naive Bayes algorithm used for?

• Clustering techniques

• Regression analysis

• The Naive Bayes algorithm is used for classification tasks.

• Data preprocessing

Explain the concept of support vector machines.

• Support Vector Machines are primarily used for regression tasks in low-dimensional space.

• Support Vector Machines are unsupervised learning models that cluster data points without labels.

• Support Vector Machines are supervised learning models that find the optimal hyperplane to separate different classes in high-dimensional space.

• Support Vector Machines rely on decision trees to classify data points.

What is a perceptron and how does it work?

• A perceptron is a hardware component that amplifies electrical signals for communication systems.

• A perceptron is a simple model of a neuron that computes a weighted sum of inputs and applies an activation function to produce a binary output.

• A perceptron is a type of software that manages data storage and retrieval in databases.

• A perceptron is a complex model that uses multiple layers of neurons to produce a continuous output.

Define ordinary least squares in regression analysis.

• Ordinary least squares is a regression analysis method that minimizes the sum of squared differences between observed and predicted values.

• A technique that uses the median of observed values to predict outcomes.

• A regression method that focuses solely on the correlation coefficient.

• A method that maximizes the sum of squared differences between observed and predicted values.

What are the advantages of using support vector machines?

• Advantages of using support vector machines include effectiveness in high-dimensional spaces, robustness against overfitting, and the ability to handle non-linear boundaries with kernel functions.

• Support vector machines are only effective in low-dimensional spaces.

• They require a large amount of labeled data to function properly.

• Support vector machines cannot handle non-linear boundaries.

How does Naive Bayes handle categorical data?

• Naive Bayes uses frequency counts of categories to calculate probabilities for classification.

• Naive Bayes ignores categorical data entirely.

• Naive Bayes requires categorical data to be transformed into binary format before processing.

• Naive Bayes converts categorical data into numerical values using one-hot encoding.