Exploring Machine Learning Concepts

What is supervised learning?

• Supervised learning is a method that requires no data for training.

• Supervised learning is a machine learning approach that uses labeled data to train models.

• Unsupervised learning uses labeled data to train models.

• Supervised learning is a type of reinforcement learning.

What is unsupervised learning?

• A method that requires labeled data for training.

• A process that eliminates noise from data before analysis.

• Unsupervised learning is a machine learning approach that finds patterns in data without labeled outputs.

• A technique used only for classification tasks.

Name one key difference between supervised and unsupervised learning.

• Supervised learning uses labeled data; unsupervised learning uses unlabeled data.

• Unsupervised learning is only used for classification tasks.

• Supervised learning can only be applied to structured data.

• Supervised learning requires more computational power than unsupervised learning.

What is classification in machine learning?

• Classification involves predicting continuous values from data points.

• Classification is an unsupervised learning method for clustering data.

• Classification is a supervised learning method used to assign labels to data points based on training data.

• Classification is a technique used to reduce the dimensionality of data.

What is regression in machine learning?

• Regression is used to classify categorical outcomes.

• Regression is an unsupervised learning technique for clustering data.

• Regression is a supervised learning technique used to predict continuous outcomes.

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

Explain linear regression in simple terms.

• Linear regression is a method for clustering data points.

• Linear regression only works with categorical data.

• Linear regression uses a curved line to fit data points.

• Linear regression predicts outcomes by fitting a straight line to data points.

What are the main steps in the machine learning process?

• 1. Define the problem, 2. Collect data, 3. Prepare data, 4. Choose model, 5. Train model, 6. Evaluate model, 7. Tune model, 8. Deploy model, 9. Monitor model.

• Select features

• Visualize data

• Analyze results

List two algorithms commonly used in classification tasks.

• Decision Trees, Support Vector Machines (SVM)

• Principal Component Analysis

• K-Means Clustering

• Linear Regression

What is the purpose of model evaluation techniques?

• The purpose of model evaluation techniques is to assess the performance and effectiveness of predictive models.

• To increase the complexity of predictive models.

• To eliminate the need for data preprocessing.

• To generate random predictions for testing purposes.

What is overfitting in the context of machine learning?

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

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

• 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.