机器学习探索计划——KNN实现Iris鸢尾花分类

1. 加载数据集

import numpy as np
from sklearn import datasets

iris = datasets.load_iris()

iris.keys()

dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])

X = iris.data
X.shape

(150, 4)

iris.feature_names

['sepal length (cm)','sepal width (cm)','petal length (cm)','petal width (cm)']

y = iris.target
y.shape

(150,)

iris.target_names

array(['setosa', 'versicolor', 'virginica'], dtype='<U10')

2.拆分数据集

shuffle_index = np.random.permutation(len(y))
shuffle_index.shape

(150,)

train_ratio = 0.8

train_size = int(len(X) * train_ratio)
train_size

120

train_index = shuffle_index[ :train_size]
test_index = shuffle_index[train_size: ]X_train = X[train_index]
y_train = y[train_index]X_test = X[test_index]
y_test = y[test_index]X_train.shape, y_train.shape, X_test.shape, y_test.shape

((120, 4), (120,), (30, 4), (30,))

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=train_ratio, random_state=666)
X_train.shape, X_test.shape, y_train.shape, y_test.shape

((120, 4), (30, 4), (120,), (30,))

3.预测

from sklearn.neighbors import KNeighborsClassifier

knn_classifier = KNeighborsClassifier(n_neighbors=5)
knn_classifier.fit(X_train, y_train)

y_predict = knn_classifier.predict(X_test)

4.评价

np.sum(y_predict==y_test)

30

accutacy = np.sum(y_predict==y_test) / len(y_test)
accutacy

1.0

from sklearn.metrics import accuracy_score

accuracy_score(y_predict, y_test)

1.0