Python - 分类套索 sklearn - 如何预测类

Question

Following the example: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html

from sklearn import linear_model
clf = linear_model.Lasso(alpha=0.1)
clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2])

clf.predict(np.array([0,0]).reshape(1,-1))
Out[13]: array([ 0.15])

Can I get the prediction to be a classification instead of a regression. In other words when I give it an input, I would like an output that is categorical.

Answer 1

Use LogisticRegression with penalty='l1' .
It is, essentially, the Lasso regression, but with the additional layer of converting the scores for classes to the "winning" class output label.
Regularization strength is defined by C , which is the INVERSE of alpha , used by Lasso .
Scikit-learn has a very nice brief overview of linear models:
https://scikit-learn.org/stable/modules/linear_model.html

Answer 2

您可以使用 Lasso 并以降序方式对预测结果进行排序，因此前 50% 将为 1，最后为 0。