11wk-42: Weighted_Data / 의사결정나무 weights

1. 강의영상

2. Imports

import numpy as np
import matplotlib.pyplot as plt
import sklearn.tree
#---#
import warnings
warnings.filterwarnings('ignore')

3. weights

# 예제1 – 아래의 예제를 생각하자.

X = np.array([1,2,3,4,5,6,7]).reshape(-1,1)
y = np.array([10,11,12,20,21,22,23])

plt.plot(X,y,'o')

- 어디에서 분기점을 나누는게 좋을까? 당연히 3.5 정도..

predictr = sklearn.tree.DecisionTreeRegressor(max_depth=1)
predictr.fit(X,y)
sklearn.tree.plot_tree(predictr);

#

# 예제2 – 아래의 예제를 생각하자.

X = np.array([1]*5000+[2]*5000+[3,4,5,6,7]).reshape(-1,1)
y = np.array([10]*5000+[11]*5000+[12,20,21,22,23])

plt.plot(X,y,'o',alpha=0.1)

- 분기점은 어디에 나누는게 좋을까? 이 경우는 1.5근처에서 나누는게 합리적으로 보임

predictr = sklearn.tree.DecisionTreeRegressor(max_depth=1)

predictr.fit(X,y)

DecisionTreeRegressor(max_depth=1)

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sklearn.tree.plot_tree(
    predictr
)

[Text(0.5, 0.75, 'x[0] <= 1.5\nsquared_error = 0.299\nsamples = 10005\nvalue = 10.505'),
 Text(0.25, 0.25, 'squared_error = 0.0\nsamples = 5000\nvalue = 10.0'),
 Text(0.75, 0.25, 'squared_error = 0.089\nsamples = 5005\nvalue = 11.009')]

#

# 예제3 – 다시 아래의 예제를 생각하자.

X = np.array([1,2,3,4,5,6,7]).reshape(-1,1)
y = np.array([10,11,12,20,21,22,23])

plt.plot(X,y,'o')
plt.plot(X[:2],y[:2],'o')

• 주황색점들을 잘 맞추는 것이 파란색점들을 잘 맞추는 것보다 5000배정도 중요하다고 상상하자.

predictr = sklearn.tree.DecisionTreeRegressor(max_depth=1)
predictr.fit(X,y,sample_weight=[5000,5000,1,1,1,1,1])
sklearn.tree.plot_tree(predictr);

#