基于KNN算法的鸢尾花种类预测

导入数据

iris_data = load_iris()
iris_data.data[0:5, :]

array([[5.1, 3.5, 1.4, 0.2],[4.9, 3. , 1.4, 0.2],[4.7, 3.2, 1.3, 0.2],[4.6, 3.1, 1.5, 0.2],[5. , 3.6, 1.4, 0.2]])

# 特征值名称
iris_data.feature_names

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

# 分类标签
print(iris_data.target_names)
pd.DataFrame(iris_data.target).value_counts()

['setosa' 'versicolor' 'virginica']0    50
1    50
2    50
dtype: int64

简单统计分析

X = pd.DataFrame(iris_data.data, columns=iris_data.feature_names)
y = iris_data.target
X.describe()

	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)
count	150.000000	150.000000	150.000000	150.000000
mean	5.843333	3.057333	3.758000	1.199333
std	0.828066	0.435866	1.765298	0.762238
min	4.300000	2.000000	1.000000	0.100000
25%	5.100000	2.800000	1.600000	0.300000
50%	5.800000	3.000000	4.350000	1.300000
75%	6.400000	3.300000	5.100000	1.800000
max	7.900000	4.400000	6.900000	2.500000

plt.figure(figsize=(3,3))
sns.heatmap(X.corr(), annot=True)

<Axes: >

plt.figure(figsize=(4,4))
sns.pairplot(X)

<seaborn.axisgrid.PairGrid at 0x16b89fbf640><Figure size 400x400 with 0 Axes>

划分数据集

x_train, x_test , y_train,y_test = train_test_split(X, y, test_size=.2 , random_state=47)
x_train.shape, y_train.shape, x_test.shape, y_test.shape

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

特征工程

归一化/标准化

transfer = StandardScaler()
x_train = transfer.fit_transform(x_train)
x_test = transfer.transform(x_test)
x_train.shape, x_test.shape

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

模型训练

knn_model = KNeighborsClassifier(n_neighbors=5)
knn_model.fit(x_train, y_train)

KNeighborsClassifier()

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KNeighborsClassifier

KNeighborsClassifier()

模型评估

y_pred = knn_model.predict(x_test)
y_pred == y_test

array([ True,  True,  True,  True,  True,  True,  True,  True,  True,True,  True,  True,  True,  True,  True,  True,  True,  True,True,  True,  True,  True,  True, False,  True,  True,  True,True,  True,  True])

knn_model.score(x_train, y_train)

0.9666666666666667

knn_model.score(x_test, y_test)

0.9666666666666667