you can also use grid.best_estimator_ (and then all the rest)
On 08/28/2017 03:07 PM, Raga Markely wrote:
Ah.. got it :D..
The pipeline was run in gridsearchcv..
It works now after calling fit..
Thanks!
Raga
On Mon, Aug 28, 2017 at 2:55 PM, Andreas Mueller <t3k...@gmail.com
<mailto:t3k...@gmail.com>> wrote:
Have you called "fit" on the pipeline?
On 08/28/2017 02:12 PM, Raga Markely wrote:
Thank you, Andreas.
When I try
pipe_lr.named_steps['clf'].coef_
I get:
AttributeError: 'LogisticRegression' object has no attribute
'coef_'
And when I try:
pipe_lr.named_steps['clf']
I get:
LogisticRegression(C=0.1, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1, max_iter=100,
multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,
solver='liblinear', tol=0.0001, verbose=0, warm_start=False)
I wonder what I am missing?
Thanks,
Raga
On Mon, Aug 28, 2017 at 12:01 PM, Andreas Mueller
<t3k...@gmail.com <mailto:t3k...@gmail.com>> wrote:
Can can get the coefficients on the scaled data with
pipeline_lr.named_steps_['clf'].coef_
though
On 08/28/2017 12:08 AM, Raga Markely wrote:
No problem, thank you!
Best,
Raga
On Mon, Aug 28, 2017 at 12:01 AM, Joel Nothman
<joel.noth...@gmail.com <mailto:joel.noth...@gmail.com>> wrote:
No, we do not have a way to get the coefficients with
respect to the input (pre-scaling) space.
On 28 August 2017 at 13:20, Raga Markely
<raga.mark...@gmail.com <mailto:raga.mark...@gmail.com>>
wrote:
Hello,
I am wondering if it's possible to get the weight
coefficients of logistic regression from a pipeline?
For instance, I have the followings:
clf_lr = LogisticRegression(penalty='l1', C=0.1)
pipe_lr = Pipeline([['sc', StandardScaler()],
['clf', clf_lr]])
pipe_lr.fit(X, y)
Does pipe_lr have an attribute that I can call to
get the weight coefficient?
Or do I have to get it from the classifier as follows?
X_std = StandardScaler().fit_transform(X)
clf_lr = LogisticRegression(penalty='l1', C=0.1)
clf_lr.fit(X_std, y)
clf_lr.coef_
Thank you,
Raga
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