<p>scikit learn的LinearRegression不计算此信息,但您可以轻松地扩展类以执行此操作:</p>
<pre><code>from sklearn import linear_model
from scipy import stats
import numpy as np
class LinearRegression(linear_model.LinearRegression):
"""
LinearRegression class after sklearn's, but calculate t-statistics
and p-values for model coefficients (betas).
Additional attributes available after .fit()
are `t` and `p` which are of the shape (y.shape[1], X.shape[1])
which is (n_features, n_coefs)
This class sets the intercept to 0 by default, since usually we include it
in X.
"""
def __init__(self, *args, **kwargs):
if not "fit_intercept" in kwargs:
kwargs['fit_intercept'] = False
super(LinearRegression, self)\
.__init__(*args, **kwargs)
def fit(self, X, y, n_jobs=1):
self = super(LinearRegression, self).fit(X, y, n_jobs)
sse = np.sum((self.predict(X) - y) ** 2, axis=0) / float(X.shape[0] - X.shape[1])
se = np.array([
np.sqrt(np.diagonal(sse[i] * np.linalg.inv(np.dot(X.T, X))))
for i in range(sse.shape[0])
])
self.t = self.coef_ / se
self.p = 2 * (1 - stats.t.cdf(np.abs(self.t), y.shape[0] - X.shape[1]))
return self
</code></pre>
<p>从<a href="https://gist.github.com/brentp/5355925">here</a>偷来的。</p>
<p>对于Python中的这种统计分析,您应该看看<a href="http://statsmodels.sourceforge.net/devel/index.html">statsmodels</a>。</p>