What Is the Meaning of Black Box in Machine Learning

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Are you new to python language or working on data analysis projects? Machine learning models to analyze data from datasets of different industries including health, education, and politics e.g, fraud analysis.

Deep learning is also a machine learning technique about people think of as an input-output machine. What is going on inside, does not matter, sometime you know the information working inside or not at all. Deep learning is basically a technology that implies a computer behaving like a machine.

The human brain contains billions of neurons that had made a connection together and understand the real working. Similar to humans, neural models have lots of perceptrons that make a model understand the features for prediction.

There is one thing to keep in mind you are working on some feature engineering for patterns. training the model on some dataset, or features are already known. You did not need to do the same process in neural networks.

Data is not in a statistical position, it changing in every second, so traditional ML fails here to determine new dataset training and testing. In other words, data engineer finds patterns in raw facts and the question arises how black box enhances and help out?

The new dataset did not need to extract features when you are using some deep learning model because black box will automatically learn the in

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