{"kind":"task","effective_mode":"full","benchmark":{"kind":"benchmark","effective_mode":"full","slug":"ds-1000","formal_name":"DS-1000","introduction":"DS-1000 builds 1,000 data-science problems from real StackOverflow questions across seven libraries including NumPy, Pandas and Matplotlib. The problems are perturbed so that recalling the original answer does not solve them.","introduction_ja":"","introduction_en":"","category":"Category not supplied","task_count":null,"acquisition_status":"Acquisition status not supplied","official_url":"https://ds1000-code-gen.github.io/","indexing_mode":"index","profile":{"resources":[],"task_format":"","scoring":"","metric":"","size":"","answer_access":"","license":"","citation":"","maintainer":"","released":"","why_hard":"","related":[],"history":"","construction":"","limitations":"","saturation":"","scores":[]}},"task_id":"3fc839fc-43b9-5da3-a87e-7dcabad697ec","task_key":"default--test--857","task_revision_id":"1","upstream_id":"857","short_description":"I am new to scikit-learn, but it did what I was hoping for. Now, maddeningly,…","config":"default","split":"test","body":"{\"library\":\"Sklearn\",\"prompt\":\"Problem:\\n\\nI 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?\\nhere is my current code\\npipe = Pipeline([\\n    (\\\"scale\\\", StandardScaler()),\\n    (\\\"model\\\", RidgeClassifier(random_state=24))\\n])\\ngrid = GridSearchCV(pipe, param_grid={\\\"model__alpha\\\": [2e-4, 3e-3, 4e-2, 5e-1]}, cv=7)\\n# where is the coef?\\n\\nAny advice is appreciated. Thanks in advance.\\n\\n\\nA:\\n\\nrunnable code\\n<code>\\nimport numpy as np\\nimport pandas as pd\\nfrom sklearn.linear_model import RidgeClassifier\\nfrom sklearn.model_selection import GridSearchCV\\nfrom sklearn.pipeline import Pipeline\\nfrom sklearn.preprocessing import StandardScaler\\nX, y = load_data()\\nassert type(X) == np.ndarray\\nassert type(y) == np.ndarray\\npipe = Pipeline([\\n    (\\\"scale\\\", StandardScaler()),\\n    (\\\"model\\\", RidgeClassifier(random_state=24))\\n])\\ngrid = GridSearchCV(pipe, param_grid={\\\"model__alpha\\\": [2e-4, 3e-3, 4e-2, 5e-1]}, cv=7)\\n</code>\\ncoef = ... # put solution in this variable\\nBEGIN SOLUTION\\n<code>\\n\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://ds1000-code-gen.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}