本文介绍了K-近邻算法(K-Nearest Neighbors, KNN)的特点与工作原理，探讨了其在处理数值和名义值数据时的优势，如高准确度及对外部数据不敏感等特性，并指出了算法存在的缺点，例如计算成本较高和内存消耗大的问题。
k-Nearest Neighbors
Pros: High accuracy, insensitive to outliers, no assumptions about data
Cons: Computationally expensive, requires a lot of memory
Pros: High accuracy, insensitive to outliers, no assumptions about data
Cons: Computationally expensive, requires a lot of memory
Works with: Numeric values, nominal values
Pseudocode for this function would look like this:
For every point in our dataset:
calculate the distance between inX and the current point
sort the distances in increasing order
take k items with lowest distances to inX
find the majority class among these items
return the majority class as our prediction for the class of inX
When dealing with values that lie in different ranges, it’s common to normalize them.
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