Knn-k Nearest Neighbor algorithm

Knn

• Simple thinking
• Less knowledge required for mathematics (nearly 0)
• Good effect
• Explains many of the details of the machine learning algorithm using the process
• A more complete process for characterizing machine learning applications

K Nearest Neighbor Essence: If two samples are similar enough, they may fall into the same category.

e.g. Green Dot is the newly added point, take its nearest K (3) point as a small group to vote, the number of votes high wins (blue than red -3:0), so green Dot should also be blue dot

Calculate Distance:

The most common Euler distance, the distance of a, b two points (two-dimensional, three-dimensional, multidimensional):

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Understanding Small Notes: ((a sample first dimension feature-B Sample First dimension feature) 2 + (a sample second dimension feature-B sample Second dimension feature) 2 + ...) again open the root of

it is almost possible to say that the KNN algorithm is the only algorithm in machine learning that does not require a training process. the input use case can be sent directly to the training data set.

• KNN can be thought of as an algorithm without a model
• To unify with other algorithms, it can be considered that the training data set itself is a model

Knn-k Nearest Neighbor algorithm