How to duplicate an estimator in order to use it on multiple data sets?

Here is an example that creates two data sets: ``` from sklearn.linear_model import LogisticRegression from sklearn.datasets import make_classification # data set 1 X1, y1 = make_classification(n_classes=2, n_features=5, random_state=1) # data set 2 X2, y2 = make_classification(n_classes=2, n_features=5, random_state=2) ``` I want to use the `LogisticRegression` estimator with the same parameter values to fit a classifier on each data set: ``` lr = LogisticRegression() clf1 = lr.fit(X1, y1) clf2 = lr.fit(X2, y2) print "Classifier for data set 1: " print " - intercept: ", clf1.intercept_ print " - coef_: ", clf1.coef_ print "Classifier for data set 2: " print " - intercept: ", clf2.intercept_ print " - coef_: ", clf2.coef_ ``` The problem is that both classifiers are the same: ``` Classifier for data set 1: - intercept: [ 0.05191729] - coef_: [[ 0.06704494 0.00137751 -0.12453698 -0.05999127 0.05798146]] Classifier for data set 2: - intercept: [ 0.05191729] - coef_: [[ 0.06704494 0.00137751 -0.12453698 -0.05999127 0.05798146]] ``` For this simple example, I could use something like: ``` lr1 = LogisticRegression() lr2 = LogisticRegression() clf1 = lr1.fit(X1, y1) clf2 = lr2.fit(X2, y2) ``` to avoid the problem. However, the question remains: How to duplicate / copy an estimator with its particular parameter values in general?