Ml quiz1

What is machine learning?

• A type of computer

• A technique for teaching computers to learn from data

• 
  A programming language

• A hardware device

What is the primary goal of supervised learning?

• Minimize errors in predictions

• Maximize computational efficiency

• Predict future events

• Learn from unlabeled data

Which of the following is an example of an unsupervised learning algorithm?

• Linear Regression

• K-Means Clustering

• Decision Trees

• Support Vector Machines

What is the purpose of feature engineering in machine learning?

• Building better hardware

• Selecting the most relevant features

• Engineering new features using deep learning

• Extracting features from images

In classification, what does the term "class label" refer to?

• 
  

• The name of the model

• The output of a regression model

• The predicted category of an input

• The input features of a model

What is the difference between precision and recall?

• Both measure the same thing

• Precision focuses on false positives, recall focuses on false negatives

• Precision focuses on false negatives, recall focuses on false positives

• Precision and recall are unrelated metrics

What is the key difference between supervised and unsupervised learning?

• The presence of labeled data in supervised learning

• The use of deep neural networks in unsupervised learning

• The requirement for large datasets in supervised learning

• The absence of algorithms in unsupervised learning

What is the primary purpose of a validation set in machine learning?

• To train the model

• To test the model

• To evaluate the model during training

• To compare multiple models

What is the purpose of a learning curve in machine learning?

• To visualize the training time of a model

• To visualize the model's accuracy over training iterations

• To visualize decision boundaries in feature space

• To visualize feature importance

How does the bias-variance trade-off impact model performance?

• Increasing bias improves model accuracy

• Increasing variance improves model generalization

• High bias can lead to underfitting, high variance to overfitting

• Low bias and low variance lead to better models