Three skills principles in machine learning basics of machine learning

The Ames Razor principle (Occam ' s Razor)

One sentence is said, "an explanation of the data should is mad as simple as possible,but no simpler".
The meaning of machine learning is that the simplest explanation of the data is the best explanation (the simplest model, fits the data is also and the most plausible).

For example, the picture above, the right is not better than the left to explain? That's obviously not the case.

Do not add entities if it is not necessary
The law of the Valeo Razor, the simple and effective principle, says, do not waste more things to do, with less things, the same can do things.

So, we tend to choose simple, less parametric assumptions than complex assumptions, and we want to choose simpler models so that the number of valid assumptions is not many.
Another explanation is that, assuming there is a simple hypothesis H, if it can distinguish a set of data well, then it is true that this set of data has some regularity.

Sampling deviation (sampling Bias)

If the data is sampled in a biased way,learning would produce a similarily biased outcome.
This sentence tells us that if the sampled data is biased, then the effect of learning is also biased, this situation is called sampling deviation.
In reality, we need training data and test data from the same distribution.
To avoid this problem, what we can do is to understand the test environment, so that the training environment or training data and test environment as close as possible.

Data snooping (snooping)

Any process you use data is indirectly prying into the data, so when you make a decision, you know that the data may have been contaminated by the complexity of the model in your mind.
The effective way to avoid this situation is to:

• Don't look at the data until you make a decision
• Always have doubts.

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GitHub home page (http://jasonding1354.github.io/)
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Three skills principles in machine learning basics of machine learning