{"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":"020780c5-3478-5d73-8e7c-12587e6895a4","task_key":"default--test--849","task_revision_id":"1","upstream_id":"849","short_description":"Say that I want to train BaggingClassifier that uses DecisionTreeClassifier:","config":"default","split":"test","body":"{\"library\":\"Sklearn\",\"prompt\":\"Problem:\\n\\nSay that I want to train BaggingClassifier that uses DecisionTreeClassifier:\\n\\ndt = DecisionTreeClassifier(max_depth = 1)\\nbc = BaggingClassifier(dt, n_estimators = 20, max_samples = 0.5, max_features = 0.5)\\nbc = bc.fit(X_train, y_train)\\nI would like to use GridSearchCV to find the best parameters for both BaggingClassifier and DecisionTreeClassifier (e.g. max_depth from DecisionTreeClassifier and max_samples from BaggingClassifier), what is the syntax for this? Besides, you can just use the default arguments of GridSearchCV.\\n\\nA:\\n\\n<code>\\nimport numpy as np\\nimport pandas as pd\\nfrom sklearn.ensemble import BaggingClassifier\\nfrom sklearn.model_selection import GridSearchCV\\nfrom sklearn.tree import DecisionTreeClassifier\\n\\nX_train, y_train = load_data()\\nassert type(X_train) == np.ndarray\\nassert type(y_train) == np.ndarray\\nX_test = X_train\\nparam_grid = {\\n    'base_estimator__max_depth': [1, 2, 3, 4, 5],\\n    'max_samples': [0.05, 0.1, 0.2, 0.5]\\n}\\ndt = DecisionTreeClassifier(max_depth=1)\\nbc = BaggingClassifier(dt, n_estimators=20, max_samples=0.5, max_features=0.5)\\n</code>\\nsolve this question with example variable `clf` and put result in `proba`\\nBEGIN SOLUTION\\n<code>\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://ds1000-code-gen.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}