So, the analysis process is done manually. You can take advantage of machine learning to analyze all customer data . Later, the system will tell which customers are eligible for loans base on their risk profile. It’s clear that the second option is the better choice. Because, in addition to the analysis not needing to take a long time, the results can be more accurate because they are base on objective historical data. So, now that you know the important role of machine learning, then we’ll talk about how it works. How does machine learning work? If describ in detail, how machine learning works is indee complex. However, there are at least three main parts that you must know about in a machine learning workflow, namely: 1. A decision process the tool the system uses to predict data is a machine learning algorithm . This algorithm needs to use a sample of data to find patterns in the data being analyzed.
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The type of data analyz can also vary, some are labele or unlabel : labele data – this is a data type that contains tags, so the categories are clear. Examples include student data tagged “class a”, “class b”, and so on. Unlabel data – in contrast, unlabeled data does not contain tags. So, the data tends to be more raw. In this section, you need to enter data into the system. So, the algorithm can predict the pattern in the data. 2. An error function by now, you might be curious. Are the pattern predictions generat by the algorithm accurate? Well, the answer is in this section. So, an error function is a method where you compare algorithm New Zealand WhatsApp Number List predictions with results that are certain to be correct. In other words, this process can be considered as a testing process. In the beginning, you already know the correct result. Then, you do an analysis to check the resulting pattern predictions.
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If the prediction is wrong, you have to measure how far it differs from the correct result. However, if the prediction is correct, it means that the algorithm model is in the right direction. 3. An optimization process model you could say this section is the process of “refining” the algorithm model that you are currently using. So, the algorithm will be evaluat continuously so that the results are more accurate. Maybe you are surprised, if in the previous process the prediction results were correct, why do you have to be test again? The answer: machine Mobile Lead learning does not stop at one point. If there is new data, of course the algorithm must learn again in order to provide accurate predictions. The analogy is like this, imagine there is a scale that has 3 kg of iron on its right side. Then, based on observation, you know that by placing a 3 kg piece of iron on the left side.
