November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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通过机器学习预测市场动向
｜
161
打印出截距值。
根据拟合的模型预测所需的值。
将参数
fit_intercept
设置为
False
可获得与
NumPy
和
polyfit()
完全相同的回归结果：
In [72]: lm = linear_model.LinearRegression(fit_intercept=False)
In [73]: lm.fit(m[:lags].T, m[lags])
Out[73]: LinearRegression(fit_intercept=False)
In [74]: lm.coef_
Out[74]: array([-0.66666667, 0.33333333, 1.33333333])
In [75]: lm.intercept_
Out[75]: 0.0
In [76]: lm.predict(m[:lags].T)
Out[76]: array([ 3., 4., 5., 6., 7., 8., 9., 10., 11.])
强制没有截距值进行拟合。
这个例子已经很好地说明了如何将
scikit-learn
应用于预测问题。由于其一致的
API
设计，基本方法也可以应用于其他模型。
5.2.2
一个简单的分类问题
在分类问题中，必须确定新观察结果属于一组有限的分类（“类别”）中的哪一个。
机器学习中研究的一个经典问题是从
0
～
9
的手写数字的识别。这种识别可能得出
正确的结果，例如
3
。或者错误的结果，例如
6
或
8
，其中所有这些错误的结果都是
同等错误的。在金融市场环境中 ...
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