Published Sep 10, 2021
SDS 504: Classification vs Regression — with Jon Krohn
Jon Krohn delves into the realms of machine learning, distinguishing between classification and regression in supervised learning. With practical examples, he elucidates how models predict outcomes, whether categorizing sentiments or recognizing digits, offering insights into the roles of these techniques in data prediction.
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Jon Krohn offers a sneak peek into the diverse categories of machine learning, setting the stage for future discussions. He hints at an upcoming segment that will explore the distinctions between supervised and unsupervised learning. This preview serves as a teaser for listeners eager to dive deeper into the complexities of machine learning 1.
Supervised Learning
Supervised learning is a fundamental category in machine learning, where models learn from labeled datasets. Jon explains that inputs, denoted as 'x', are processed to predict outcomes, labeled as 'y'. He illustrates this with an example of movie reviews, where the input is the review text and the label indicates whether the review is positive or negative 2.
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