Python机器学习及实践（一、分类学习——K近邻分类）

Python机器学习及实践（一、分类学习——K近邻分类）

K近邻简介

K最近邻(k-Nearest Neighbor，KNN)分类算法，是一个理论上比较成熟的方法，也是最简单的机器学习算法之一。该方法的思路是：如果一个样本在特征空间中的k个最相似(即特征空间中最邻近)的样本中的大多数属于某一个类别，则该样本也属于这个类别。

代码及注释

输入：

from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
iris = load_iris()
print(iris.data.shape)
print(iris.DESCR)
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size = 0.25, random_state = 33)
print(X_train.shape)
from sklearn.preprocessing import StandardScaler
ss=StandardScaler()
X_train=ss.fit_transform(X_train)
X_test=ss.transform(X_test)
from sklearn.neighbors import KNeighborsClassifier
knc=KNeighborsClassifier()
knc.fit(X_train,y_train)
y_predict=knc.predict(X_test)
from sklearn.metrics import classification_report
print('Accuracy of Mulitinomial:',knc.score(X_test,y_test))
print(classification_report(y_test,y_predict,target_names=iris.target_names))

输出：

(150, 4)
.. _iris_dataset:

Iris plants dataset
--------------------

**Data Set Characteristics:**

    :Number of Instances: 150 (50 in each of three classes)
    :Number of Attributes: 4 numeric, predictive attributes and the class
    :Attribute Information:
        - sepal length in cm
        - sepal width in cm
        - petal length in cm
        - petal width in cm
        - class:
                - Iris-Setosa
                - Iris-Versicolour
                - Iris-Virginica
                
    :Summary Statistics:

    ============== ==== ==== ======= ===== ====================
                    Min  Max   Mean    SD   Class Correlation
    ============== ==== ==== ======= ===== ====================
    sepal length:   4.3  7.9   5.84   0.83    0.7826
    sepal width:    2.0  4.4   3.05   0.43   -0.4194
    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)
    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)
    ============== ==== ==== ======= ===== ====================

    :Missing Attribute Values: None
    :Class Distribution: 33.3% for each of 3 classes.
    :Creator: R.A. Fisher
    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
    :Date: July, 1988

The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken
from Fisher's paper. Note that it's the same as in R, but not as in the UCI
Machine Learning Repository, which has two wrong data points.

This is perhaps the best known database to be found in the
pattern recognition literature.  Fisher's paper is a classic in the field and
is referenced frequently to this day.  (See Duda & Hart, for example.)  The
data set contains 3 classes of 50 instances each, where each class refers to a
type of iris plant.  One class is linearly separable from the other 2; the
latter are NOT linearly separable from each other.

.. topic:: References

   - Fisher, R.A. "The use of multiple measurements in taxonomic problems"
     Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
     Mathematical Statistics" (John Wiley, NY, 1950).
   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.
     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.
   - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
     Structure and Classification Rule for Recognition in Partially Exposed
     Environments".  IEEE Transactions on Pattern Analysis and Machine
     Intelligence, Vol. PAMI-2, No. 1, 67-71.
   - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions
     on Information Theory, May 1972, 431-433.
   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al"s AUTOCLASS II
     conceptual clustering system finds 3 classes in the data.
   - Many, many more ...
(112, 4)
Accuracy of Mulitinomial: 0.8947368421052632
              precision    recall  f1-score   support

      setosa       1.00      1.00      1.00         8
  versicolor       0.73      1.00      0.85        11
   virginica       1.00      0.79      0.88        19

    accuracy                           0.89        38
   macro avg       0.91      0.93      0.91        38
weighted avg       0.92      0.89      0.90        38

知识点

1.Iris数据集

这个数据集可以从UCI数据集上直接下载，具体地址为：http://archive.ics.uci.edu/ml/datasets/Iris。打开页面后点击Data folder就可以下载到本地磁盘上，默认格式为逗号分隔的文本文件。也可以直接从sklearn包里datasets里导入

2.print(iris.DESCR)

DESCR用于查看数据说明

3.from sklearn.neighbors import KNeighborsClassifier

K近邻分类方法

KNeighborsClassifier(n_neighbors=5, weights=’uniform’, algorithm=’auto’, leaf_size=30, p=2, metric=’minkowski’, metric_params=None, n_jobs=1, **kwargs)

重要的参数：
n_neighbors: 近邻数量,默认为5
leaf_size :最大叶子数，会影响性能与内存
n_jobs : 并行参数
该方法的具体理解看链接：https://blog.csdn.net/tianweidadada/article/details/80305706

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