pythonML

ML Practice 2_1

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ML Algorithm

Supervised Learning(지도 학습)

  • Input(입력; independent variable) & Target(타깃; dependent variable)
  • Question with a correct answer
    • Type 1: Classification(분류)
    • Type 2: Regression(예측)
  • Feature(특성) = independent variable(column)

Unspervised Learning(비지도 학습)

  • only Input, not Target
  • Question without an answer
  • algorithm automatically categorizes

Data set

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fish_length = [25.4, 26.3, 26.5, 29.0, 29.0, 29.7, 29.7, 30.0, 30.0, 30.7, 31.0, 31.0, 
                31.5, 32.0, 32.0, 32.0, 33.0, 33.0, 33.5, 33.5, 34.0, 34.0, 34.5, 35.0, 
                35.0, 35.0, 35.0, 36.0, 36.0, 37.0, 38.5, 38.5, 39.5, 41.0, 41.0, 9.8, 
                10.5, 10.6, 11.0, 11.2, 11.3, 11.8, 11.8, 12.0, 12.2, 12.4, 13.0, 14.3, 15.0]
fish_weight = [242.0, 290.0, 340.0, 363.0, 430.0, 450.0, 500.0, 390.0, 450.0, 500.0, 475.0, 500.0, 
                500.0, 340.0, 600.0, 600.0, 700.0, 700.0, 610.0, 650.0, 575.0, 685.0, 620.0, 680.0, 
                700.0, 725.0, 720.0, 714.0, 850.0, 1000.0, 920.0, 955.0, 925.0, 975.0, 950.0, 6.7, 
                7.5, 7.0, 9.7, 9.8, 8.7, 10.0, 9.9, 9.8, 12.2, 13.4, 12.2, 19.7, 19.9]
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fish_data = [[l,w] for l, w in zip(fish_length, fish_weight)]
fish_target = [1]*35 + [0]*14 # 1: bream, 0: smelt

KNN 1

Create KNN

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from sklearn.neighbors import KNeighborsClassifier
kn = KNeighborsClassifier()

Data Split

  • train set & test set
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train_input = fish_data[:35]
train_target = fish_target[:35]
test_input = fish_data[35:]
test_target = fish_target[35:]

Model fitting

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kn = kn.fit(train_input, train_target)
kn.score(test_input, test_target) # Sampling bias
0.0

KNN 2

Numpy array

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import numpy as np

input_arr = np.array(fish_data)
target_arr = np.array(fish_target)

input_arr.shape, target_arr.shape
((49, 2), (49,))

Data Shuffle and Split

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np.random.seed(42)
index = np.arange(49)
print(index)
np.random.shuffle(index)
print(index)
[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
 48]
[13 45 47 44 17 27 26 25 31 19 12  4 34  8  3  6 40 41 46 15  9 16 24 33
 30  0 43 32  5 29 11 36  1 21  2 37 35 23 39 10 22 18 48 20  7 42 14 28
 38]

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train_input = input_arr[index[:35]]
train_target = target_arr[index[:35]]
test_input = input_arr[index[35:]]
test_target = target_arr[index[35:]]

Scatter Plot

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import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.scatter(train_input[:,0],train_input[:,1])
ax.scatter(test_input[:,0],test_input[:,1])
ax.set_xlabel('length')
ax.set_ylabel('weight')
plt.show()

Model fitting

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kn = kn.fit(train_input, train_target)
kn.score(test_input, test_target)
1.0

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print(kn.predict(test_input) == test_target)
[ True  True  True  True  True  True  True  True  True  True  True  True
  True  True]

Ref.) 혼자 공부하는 머신러닝+딥러닝 (박해선, 한빛미디어)

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