ai learning algorithms

the fast row is n^2, if each time is equal division that is T (n) =t (N/2) +t (n /2) +o (n), then the complexity of time is NLGN. However, these two divisions are not common in peacetime, so they are not representative; But if we examine some of the imbalances, such as 9:1, T (n) =t (N/10) +t (N*9/10) +o (n), we can see that in this case the time complexity is still NLGN , and even if the imbalance is further increased (as long as it is not completely unbalanced), to a degree of 99:1, the time

Continuous update ...1.k-Nearest Neighbor algorithmAdvantages: High precision, insensitive to outliers, no data input settingsCons: High computational complexity, high spatial complexityApplicable data range: Numerical and nominal typeApplicable scenarios:2.ID3 Decision Tree AlgorithmAdvantages: The computational complexity is not high, the output is easy to understand, the missing middle value is not sensitive, can process the irrelevant characteristic dataDisadvantage: May cause over-matching