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DS-1000 / 857 / I am new to scikit-learn, but it did what I was hoping for. Now, maddeningly,…

Problem

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Problem:

I am new to scikit-learn, but it did what I was hoping for. Now, maddeningly, the only remaining issue is that I don't find how I could print the model's coefficients it estimated. Especially when it comes to a pipeline fitted by a GridSearch. Now I have a pipeline including data scaling, centering, and a classifier model. What is the way to get its estimated coefficients?
here is my current code
pipe = Pipeline([
    ("scale", StandardScaler()),
    ("model", RidgeClassifier(random_state=24))
])
grid = GridSearchCV(pipe, param_grid={"model__alpha": [2e-4, 3e-3, 4e-2, 5e-1]}, cv=7)
# where is the coef?

Any advice is appreciated. Thanks in advance.


A:

runnable code
<code>
import numpy as np
import pandas as pd
from sklearn.linear_model import RidgeClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
X, y = load_data()
assert type(X) == np.ndarray
assert type(y) == np.ndarray
pipe = Pipeline([
    ("scale", StandardScaler()),
    ("model", RidgeClassifier(random_state=24))
])
grid = GridSearchCV(pipe, param_grid={"model__alpha": [2e-4, 3e-3, 4e-2, 5e-1]}, cv=7)
</code>
coef = ... # put solution in this variable
BEGIN SOLUTION
<code>

library

Sklearn

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Source and history

upstream_id / Original problem ID
857
task_id
3fc839fc-43b9-5da3-a87e-7dcabad697ec
task_key
default--test--857
task_revision_id
1
config / split
default / test

Official source

initial import