The outcome is either animal or not an animal-there is no range in between. Logistic regression requires that the dependent variable, in this case whether the item was an animal or not, be categorical. Similarly, knowing that the orange had zero limbs would help as well. In this way, knowing that an orange’s shape was a circle may help the algorithm to conclude that the orange was not an animal. Aspects, or features, may include color, size, weight, shape, height, volume or amount of limbs.
#Jmp logistic regression how to#
Other similar aspects of these items would need to be looked at when considering how to classify each item or data point. To dive a little deeper into how your model might attempt to classify these two items directly, let’s consider what else the model would need to know about the items in order to decide where they belong. However, it is also possible to set up your logistic regression with more than two possible categories (multinomial logistic regression). In this example, there are only two possible answers (binary logistic regression), animal or not an animal. Animal is your target it is dependent on your data in order to be able to classify the item correctly. In essence, if you have a large set of data that you want to categorize, logistic regression may be able to help.įor example, if you were given a dog and an orange and you wanted to find out whether each of these items was an animal or not, the desired result would be for the dog to end up classified as an animal, and for the orange to be categorized as not an animal. Logistic regression is a supervised learning algorithm used to predict a dependent categorical target variable.
#Jmp logistic regression software#
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