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Post by account_disabled on Dec 14, 2023 3:59:52 GMT -5
A machine learning algorithm is a procedure performed on data to create a machine learning model. A machine learning model is the result of running an ML algorithm on data. In other words, a model represents what has been learned by a machine learning algorithm. What are the main differences between supervised and unsupervised learning? If we had to summarize it in one sentence, the main difference between supervised learning and unsupervised learning is that the former. Unlike unsupervised learning, uses labeled data to help predict outcomes. However, there are also other nuances between the two approaches, which we will analyze in the next paragraphs so that you can choose the one best suited to your Job Function Email List needs. How supervised machine learning works As we mentioned earlier, supervised learning uses labeled data to train the model. But what exactly does that mean? Let's see some examples. With supervised learning, the model receives both corresponding inputs and outputs. Suppose we are training the model to identify and classify different types of fruit. We will provide several fruit images as input, along with information such as shape, size, color and flavor profile. Next, we will provide the model with the names of each fruit as output. Finally, the algorithm will recognize a pattern that combines the characteristics of the fruits (the inputs) with their names (the outputs). At this point, we can give the model a new input to make it predict the output on its own.
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