使用具有不同 k 值的 k-nn 绘制图形

Question

我想为 k-nn 分类器绘制具有不同 k 值的图形。我的问题是这些数字似乎具有相同的 k 值。到目前为止，我尝试过的是更改循环中每个运行中的 k 值：

clf = KNeighborsClassifier(n_neighbors=counter+1) 但是所有的数字似乎都是 k=1

from sklearn.datasets import fetch_california_housing
data = fetch_california_housing()
import numpy as np
from sklearn.model_selection import train_test_split

c = np.array([1 if y > np.median(data['target']) else 0 for y in data['target']])
X_train, X_test, c_train, c_test = train_test_split(data['data'], c, random_state=0)

from sklearn.neighbors import KNeighborsClassifier
import mglearn
import matplotlib.pyplot as plt

fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(20, 6))
for counter in range(3):      
    clf = KNeighborsClassifier(n_neighbors=counter+1) 
    clf.fit(X_test, c_test)
    plt.tight_layout()  # this will help create proper spacing between the plots.
    mglearn.discrete_scatter(X_test[:,0], X_test[:,1], c_test, ax=ax[counter])
    plt.legend(["Class 0", "Class 1"], loc=4)
    plt.xlabel("First feature")
    plt.ylabel("Second feature")
    #plt.figure()

Answer 1

所有图看起来都一样的原因是您每次都只是简单地绘制测试集，而不是绘制测试集上的模型预测。您可能打算对 k 的每个值执行以下操作：

将模型拟合到训练集，在这种情况下，您应该将 clf.fit(X_test, c_test) 替换为 clf.fit(X_train, c_train)。
在测试集上生成模型预测，在这种情况下您应该添加 c_pred = clf.predict(X_test).
在测试集上绘制模型预测，在这种情况下，您应该在散点图中将 c_test 替换为 c_pred，即使用 mglearn.discrete_scatter(X_test[:, 0], X_test[:, 1], c_pred, ax=ax[counter]) 而不是mglearn.discrete_scatter(X_test[:, 0], X_test[:, 1], c_test, ax=ax[counter]).

更新代码：

from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
import mglearn
import matplotlib.pyplot as plt

data = fetch_california_housing()

c = np.array([1 if y > np.median(data['target']) else 0 for y in data['target']])

X_train, X_test, c_train, c_test = train_test_split(data['data'], c, random_state=0)

fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(20, 6))

for counter in range(3):

    clf = KNeighborsClassifier(n_neighbors=counter+1)

    # fit the model to the training set
    clf.fit(X_train, c_train)

    # extract the model predictions on the test set
    c_pred = clf.predict(X_test)

    # plot the model predictions
    plt.tight_layout()
    mglearn.discrete_scatter(X_test[:,0], X_test[:,1], c_pred, ax=ax[counter])
    plt.legend(["Class 0", "Class 1"], loc=4)
    plt.xlabel("First feature")
    plt.ylabel("Second feature")

使用具有不同 k 值的 k-nn 绘制图形

Draw figures using k-nn with different values of k

python

machine-learning

knn