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31 lines
902 B
31 lines
902 B
# Logistic Regression
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自己编程实现Logistic Regression的多分类问题。使用的数据可以是sklearn的digital数据。
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加载数据的方式是：
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```
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import matplotlib.pyplot as plt
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from sklearn.datasets import load_digits
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# load data
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digits = load_digits()
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# plot the digits
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fig = plt.figure(figsize=(6, 6)) # figure size in inches
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fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
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# plot the digits: each image is 8x8 pixels
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for i in range(64):
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ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])
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ax.imshow(digits.images[i], cmap=plt.cm.binary)
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# label the image with the target value
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ax.text(0, 7, str(digits.target[i]))
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```
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要求：
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1. 自己编程实现Logistic Regression的多分类。
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2. 对比自己实现与sklearn的方法的精度。
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3. 如何将分类错误的样本可视化出来？ |