WorksheetsApplying AI Techniques
Total questions: 15
Worksheet time: 5mins
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.
The algorithm makes predictions based on the data it processes.
(a)
It can be used in medicine to help
diagnose diseases based on patient data.
(a)
It can group patients with similar symptoms or
conditions, helping to spot treatment approaches or predict health risks.
(a)
It is a type of machine learning that learns through trial and error, improving over time by reinforcing successful outcomes.
(a)
It uses mathematical techniques to predict continuous
outcomes based on one or more predictor variables.
(a)
