机器学习入门实战——KNN实战Iris数据集

KNN实战Iris数据集

关于KNN的相关理论知识请查看：KNN

关于Iris数据集的相关信息可查看我的上一篇博客：感知机算法实战Iris数据集

接下来的实战我们将使用sklearn库

代码实战

首先，我们还是先导入数据集

from sklearn.datasets import load_iris
iris = load_iris()
iris.data.shape

(150, 4)

sklearn的datasets都有详细的数据集信息，我们可以把这些信息打印出来查看

print(iris.DESCR)

Iris Plants Database

Notes

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%[email protected])
:Date: July, 1988

This is a copy of UCI ML iris datasets.
http://archive.ics.uci.edu/ml/datasets/Iris

The famous Iris database, first used by Sir R.A Fisher

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.

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 …

按照一般机器学习的套路，我们将整个数据分成训练集和测试集，75%的训练集和25%的测试集

from sklearn.cross_validation import train_test_split
X_train,X_test,Y_train,Y_test = train_test_split(iris.data,iris.target,test_size=0.25,random_state=33)

下一步，我们将数据进行标准化处理，然后导入KNN模型，进行训练（这些都是套路）

from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier

ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.fit_transform(X_test)

knc = KNeighborsClassifier()
knc.fit(X_train,Y_train)
y_predict = knc.predict(X_test)

最后，我们来检验一下模型的好坏

print('The accuracy of K-Nearest Neighbor Classifier is',knc.score(X_test,Y_test))

from sklearn.metrics import classification_report
print(classification_report(Y_test,y_predict,target_names=iris.target_names))

代码参考：《Python机器学习及实践：从零开始通往Kaggle竞赛之路》