The coef_ attribute of the LogisticRegression object stores the parameters.
Andrew <~~~~~~~~~~~~~~~~~~~~~~~~~~~> J. Andrew Howe, PhD LinkedIn Profile <http://www.linkedin.com/in/ahowe42> ResearchGate Profile <http://www.researchgate.net/profile/John_Howe12/> Open Researcher and Contributor ID (ORCID) <http://orcid.org/0000-0002-3553-1990> Github Profile <http://github.com/ahowe42> Personal Website <http://www.andrewhowe.com> I live to learn, so I can learn to live. - me <~~~~~~~~~~~~~~~~~~~~~~~~~~~> On Sat, Jun 8, 2019 at 6:58 PM Eric J. Van der Velden < ericjvandervel...@gmail.com> wrote: > Here I have added what I had programmed. > > With sklearn's LogisticRegression(), how can I see the parameters it has > found after .fit() where the cost is minimal? I use the book of Geron about > scikit-learn and tensorflow and on page 137 he trains the model of petal > widths. I did the following: > > iris=datasets.load_iris() > a1=iris['data'][:,3:] > y=(iris['target']==2).astype(int) > log_reg=LogisticRegression() > log_reg.fit(a1,y) > > log_reg.coef_ > array([[2.61727777]]) > log_reg.intercept_ > array([-4.2209364]) > > > I did the logistic regression myself with Gradient Descent or > Newton-Raphson as I learned from my Coursera course and respectively from > my book of Bishop. I used the Gradient Descent method like so: > > from sklearn import datasets > iris=datasets.load_iris() > a1=iris['data'][:,3:] > A1=np.c_[np.ones((150,1)),a1] > y=(iris['target']==2).astype(int).reshape(-1,1) > lmda=1 > > from scipy.special import expit > > def logreg_gd(w): > z2=A1.dot(w) > a2=expit(z2) > delta2=a2-y > w=w-(lmda/len(a1))*A1.T.dot(delta2) > return w > > w=np.array([[0],[0]]) > for i in range(0,100000): > w=logreg_gd(w) > > In [6219]: w > Out[6219]: > array([[-21.12563996], > [ 12.94750716]]) > > I used Newton-Raphson like so, see Bishop page 207, > > from sklearn import datasets > iris=datasets.load_iris() > a1=iris['data'][:,3:] > A1=np.c_[np.ones(len(a1)),a1] > y=(iris['target']==2).astype(int).reshape(-1,1) > > def logreg_nr(w): > z1=A1.dot(w) > y=expit(z1) > R=np.diag((y*(1-y))[:,0]) > H=A1.T.dot(R).dot(A1) > tmp=A1.dot(w)-np.linalg.inv(R).dot(y-t) > v=np.linalg.inv(H).dot(A1.T).dot(R).dot(tmp) > return v > > w=np.array([[0],[0]]) > for i in range(0,10): > w=logreg_nr(w) > > In [5149]: w > Out[5149]: > array([[-21.12563996], > [ 12.94750716]]) > > Notice how much faster Newton-Raphson goes than Gradient Descent. But they > give the same result. > > How can I see which parameters LogisticRegression() found? And should I > give LogisticRegression other parameters? > > On Sat, Jun 8, 2019 at 11:34 AM Eric J. Van der Velden < > ericjvandervel...@gmail.com> wrote: > >> Hello, >> >> I am learning sklearn from my book of Geron. On page 137 he learns the >> model of petal widths. >> >> When I implements logistic regression myself as I learned from my >> Coursera course or from my book of Bishop I find that the following >> parameters are found where the cost function is minimal: >> >> In [6219]: w >> Out[6219]: >> array([[-21.12563996], >> [ 12.94750716]]) >> >> I used Gradient Descent and Newton-Raphson, both give the same answer. >> >> My question is: how can I see after fit() which parameters >> LogisticRegression() has found? >> >> One other question also: when I read the documentation page, >> https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression, >> I see a different cost function as I read in the books. >> >> Thanks. >> >> >> >> _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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