HOTSPOT (Drag and Drop is not supported)Select the answer that correctly completes the sentence.Hot Area:
Answer(s): A
Box: featuresData values that are used to make a prediction are called ________.In the context of machine learning and predictive modeling, data values that are used as input variables to make predictions are referred to as "features." Features are essential components of a dataset and play a crucial role in training and building machine learning modelsIncorrect:* dependent variablesThe values used to make the prediction are called the predictor or *independent variables*. Predictive models are built, or trained, using data for which the value of the response variable is already known.
https://brainly.com/question/38736383
What is an example of unsupervised machine learning?
Answer(s): C
Unsupervised learning is a type of machine learning algorithm where insights are generated from data without any dependent variable. There are several use cases of unsupervised learning. The most popular use case is market segmentation, where you divide the market or customer groups into different clusters. This helps in building targeted marketing strategy. Another application of unsupervised learning is in association mining or building recommendation engines. The most common unsupervised machine learning technique is k-means clustering.Note: K-means clustering is an unsupervised machine learning algorithm that is used to group together similar items based on a similarity metric. The K-Means Clustering module is used in Azure Machine Learning Studio to configure and create a k-means clustering model.
https://www.pluralsight.com/resources/blog/guides/unsupervised-machine-learning-in-azure-machine-learning- studio
You need to predict the population size of a specific species of animal in an area.Which Azure Machine Learning type should you use?
Answer(s): B
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable. You use this component to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/linear-regression
Which term is used to describe uploading your own data to customize an Azure OpenAI model?
Grounding is the process of using large language models (LLMs) with information that is use-case specific, relevant, and not available as part of the LLM's trained knowledge. It is crucial for ensuring the quality, accuracy, and relevance of the generated output. While LLMs come with a vast amount of knowledge already, this knowledge is limited and not tailored to specific use-cases. To obtain accurate and relevant output, we must provide LLMs with the necessary information. In other words, we need to "ground" the models in the context of our specific use-case.
https://techcommunity.microsoft.com/t5/fasttrack-for-azure/grounding-llms/ba-p/3843857
HOTSPOT (Drag and Drop is not supported)To complete the sentence, select the appropriate option in the answer area.Hot Area:
Data values that are used to make a prediction are called features. In machine learning, features are the input variables or attributes that the model uses to learn patterns and make predictions.
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