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()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
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
