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Now we’ll add one more character in our example. Say you know that Sarah scored 94 in History, 82 in Math, … and now you want to predict how successful she will be in 5 years. This type of problem is called a “classification problem” as you classify an object as either belonging in a group (successful) or not. Logistic regression is particularly good at solving these. Logistic regression makes predictions using probability (there is substantial debate on understanding exactly what probability means, for our understanding it’ll be sufficient if you know this Penn State Semifinals Bound 2023 Men’s Lacrosse championships shirt moreover I will buy this much): You have your scores (independent variable), you also know whether a person succeeds or not (dependent variable). You then somehow [1] come up with predictions and you look at how well your predictions align with your recorded data.
Say you predicted 0.9 on Ben, and in the Penn State Semifinals Bound 2023 Men’s Lacrosse championships shirt moreover I will buy this same manner you’re pretty close in all your predictions then you have a very developed a pretty good model. On the contrary you could also predict 0.2 on Ben, then your model is way off in predicting whether Ben succeeded or not. We go about looking at various models[2] (of course not randomly) and find out the model which fits very closely with our recorded data. The step by which we arrive at a model is called “model selection”. Then you plug in Sarah’s (and also everyone in your current class if you wish) scores into this model and it spits out a number between 0 and 1. By looking at this, if it is greater than 0.5 you say you predict person is successful. If it is less than 0.5 you’ll say they might not be successful.
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