KNN演算法進行鳶尾花分類

import numpy as np

import matplotlib.pylab as pyb
%matplotlib inline
from sklearn.neighbors import kneighborsclassifier
from sklearn import datasets
from sklearn.model_selection import train_test_split

x, y = datasets.load_iris(

true
)x = x[:,
:2]x_train,x_test, y_train, y_test = train_test_split(x, y, train_size =
1, random_state =
100)
x_test

pyb.scatter(x[:,

0],x[:,1
], c =y)
pyb.show(
)

x1 = np.linspace(4,

8,100)
y1 = np.linspace(2,
4.5,80)
x1,y1 = np.meshgrid(x1, y1)
x1 = x1.reshape(-1
,1)y1 = y1.reshape(-1
,1)data = np.concatenate(
(x1,y1)
,axis=1)
data.shape

(8000, 2)

%

%time
knn = kneighborsclassifier(n_neighbors=5)
knn.fit(x_train, y_train)

wall time: 3 ms

kneighborsclassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=none, n_jobs=none, n_neighbors=5, p=2,
weights='uniform')

y_ = knn.predict(data)

from matplotlib.colors import listedcolormap

lc = listedcolormap(
['#ffaaaa'
,'#aaffaa'
,'#aaaaff'])
lc2 = listedcolormap(
['#ff0000'
,'#00ff00'
,'#0000ff'
])

pyb.scatter(data[:,

0],data[:,
1],c = y_, cmap=lc)
pyb.scatter(x_train[:,
0],x_train[:,
1],c = y_train, cmap=lc2)
pyb.show(
)

pyb.contourf(x1.reshape(80,

100)
,y1.reshape(80,
100)
,y_.reshape(80,
100)
,cmap=lc)
pyb.scatter(x_train[:,
0],x_train[:,
1],c = y_train, cmap=lc2)
pyb.show(
)

from sklearn.model_selection import cross_val_score

knn = kneighborsclassifier(

)score = cross_val_score(knn,x, y, scoring=
'accuracy'
,cv =6)
score.mean(
)

0.7466666666666666

from sklearn.model_selection import cross_val_score

x,y = datasets.load_iris(
true
)erros =
weights =
['distance'
,'uniform'
]for k in
range(1
,14):
for w in weights:
knn = kneighborsclassifier(n_neighbors=k,weights=w)
score = cross_val_score(knn,x,y,scoring=
'accuracy'
,cv =6)
.mean(
)# 誤差越小，說明k選擇越合適，越好
erros[w+
str(k)
]= score
erros

list

(erros)

['distance1',

'uniform1',
'distance2',
'uniform2',
'distance3',
'uniform3',
'distance4',
'uniform4',
'distance5',
'uniform5',
'distance6',
'uniform6',
'distance7',
'uniform7',
'distance8',
'uniform8',
'distance9',
'uniform9',
'distance10',
'uniform10',
'distance11',
'uniform11',
'distance12',
'uniform12',
'distance13',
'uniform13']

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