sklearn里面knn调参数以及分类器效果评估方法

当然文档写的很好了，多而全面， 我这里只是针对自己情况做个学习的记录
http://scikit-learn.org/stable/modules/neighbors.html#classification
http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html

1. 调参数

class sklearn.neighbors.KNeighborsClassifier(n_neighbors=5, weights=’uniform’, algorithm=’auto’, leaf_size=30, p=2, metric=’minkowski’, metric_params=None, n_jobs=1, **kwargs)
n_neighbors：选取几个邻居
weights：邻居的权重是平均呢，还是越重话语权越大呢
algorithm：后面再展开
- ‘ball_tree’ will use BallTree
- ‘kd_tree’ will use KDTree
- ‘brute’ will use a brute-force search.
- ‘auto‘默认选项，是看哪个好用那个
leaf