dataset
在scikit-learn包自带的数据集，R包也自带数据集iris鸢尾花数据集，做训练集。特征较少。
from sklearn import datasetsKNN（K-近邻算法）
一个非常简单的算法，主要是原理简单
有写好的API可以直接用，然后调参。
记得基本knn的貌似误差很大，这里可以用智能算法优化，可以想一下啊，我记得是有paper的，看下思路
KNeighborsClassifier(n_neighbors=5, weights=’uniform’, algorithm=’auto’, leaf_size=30,p=2, metric=’minkowski’, metric_params=None, n_jobs=1, **kwargs)基本参数含义
- n_neighbors : int, optional (default = 5) Number of neighbors to use by default for kneighbors queries…
- weights : str or callable, optional (default = ‘uniform’)weight function used in prediction. Possible values::
- ‘uniform’ : uniform weights. All points in each neighborhood are weighted equally.
- ‘distance’ : weight points by the inverse of their distance. in this case, closer neighbors of a query point will have a greater influence
than neighbors which are further away. - [callable] : a user-defined function which accepts an array of distances, and returns an array of the same shape containing the weights.
- algorithm : {‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, optional
Algorithm used to compute the nearest neighbors:
- ‘ball_tree’ will use BallTree
- ‘kd_tree’ will use KDTree
- ‘brute’ will use a brute-force search.
- ‘auto’ will attempt to decide the most appropriate algorithm based on the values passed to fit method.
auto是用户自定义
Note: fitting on sparse input will override the setting of this parameter, using brute force.
leaf_size : int, optional (default = 30)Leaf size passed to BallTree or KDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem.
- p : integer, optional (default = 2)Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
这个就是选择不同的距离度量方式，不过常见的欧式距离比较多啊 metric : string or callable, default ‘minkowski’the distance metric to use for the tree. The default metric is minkowski, and with p=2 is equivalent to the standard Euclidean metric. See the documentation of the DistanceMetric class for a list of available metrics.
metric_params : dict, optional (default = None)Additional keyword arguments for the metric function.
n_jobs : int or None, optional (default=None)The number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details. Doesn’t affect fit method.