Implementation of the concept code of K neighbor classifier based on sklearn

Implementation of the concept code of K neighbor classifier based on sklearn


The KNN (K Nearest) classifier should be regarded as a relatively simple machine learning algorithm of the probability school. The basic idea is to calculate the Euclidean distance (geometric distance) from the input vector to each training sample when predicting, and select the nearest K training samples. The category that appears the most among the K training samples is the category predicted as the input vector ( vote)


Load data set-iris flower data set

from sklearn.datasets import load_iris
dataset = load_iris()
(150, 4)
Iris Plants Database

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
    :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: RA Fisher
    :Donor: Michael Marshall (
    :Date: July, 1988

This is a copy of UCI ML iris datasets.

The famous Iris database, first used by Sir RA 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.

   -Fisher,RA "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,RO, & Hart,PE (1973) Pattern Classification and Scene Analysis.
     (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
   -Dasarathy, BV (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, GW (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 ...

Data preprocessing

Split data

from sklearn.cross_validation import train_test_split
x_train,x_test,y_train,y_test = train_test_split(,,test_size=0.25,random_state=1)
(112, 4)
(38, 4)


from sklearn.preprocessing import StandardScaler
stantard = StandardScaler()
x_train = stantard.fit_transform(x_train)
x_test = stantard.transform(x_test)

Call K proximity classifier

from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(),y_train)
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
           metric_params=None, n_jobs=1, n_neighbors=5, p=2,

Model evaluation



Evaluator evaluation

from sklearn.metrics import classification_report
y_pre = knn.predict(x_test)
             precision recall f1-score support

     setosa 1.00 1.00 1.00 13
 versicolor 1.00 0.94 0.97 16
  virginica 0.90 1.00 0.95 9

avg/total 0.98 0.97 0.97 38

Reference: Concept code implementation of K proximity classifier based on sklearn-Cloud + Community-Tencent Cloud