Applying AI Techniques

What is the primary goal of supervised learning?

• To classify data into predefined categories without labels.

• To predict future outcomes without any training data.

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

• To optimize the performance of unsupervised learning algorithms.

What distinguishes unsupervised learning from supervised learning?

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

• Unsupervised learning is only applicable to classification tasks.

• Both unsupervised and supervised learning use labeled data equally.

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

What type of machine learning technique uses labeled data?

• Reinforcement learning

• Unsupervised learning

• Supervised learning

• Semi-supervised learning

Which of the following is a common regression algorithm?

• Logistic Regression

• Decision Trees

• Support Vector Machines

• Linear Regression

What is the main purpose of semi-supervised learning?

• To reduce the amount of data needed for training.

• To eliminate the need for any data labeling.

• To use only labeled data for training.

• To enhance learning performance using both labeled and unlabeled data.

How does clustering relate to unsupervised learning?

• Clustering is a method used to predict future outcomes based on past data.

• Clustering is a method in unsupervised learning that groups similar data points without labeled outcomes.

• Clustering is only applicable to numerical data and not categorical data.

• Clustering is a supervised learning technique that requires labeled data.

Which machine learning technique is best for predicting continuous outcomes?

• Classification techniques, such as decision trees.

• Clustering methods, like k-means.

• Dimensionality reduction techniques, such as PCA.

• Regression techniques, such as linear regression.

What is the difference between classification and regression?

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

• Classification predicts categories; regression predicts continuous values.

• Classification predicts numerical values; regression predicts categories.

• Classification requires more data than regression.

What type of data is typically used in unsupervised learning?

• Labeled data

• Structured data

• Time series data

• Unlabeled data

What is the significance of the training set in supervised learning?

• The training set is used to validate the model's performance.

• The training set is irrelevant to the learning process.

• The training set contains only unlabelled data for testing.

• The training set is significant because it enables the model to learn from labeled examples.