{"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":"495c42b5-ff80-58a0-bb68-5d4fecd710c3","task_key":"default--test--844","task_revision_id":"1","upstream_id":"844","short_description":"I have used sklearn for Cross-validation and want to do a more visual…","config":"default","split":"test","body":"{\"library\":\"Sklearn\",\"prompt\":\"Problem:\\n\\nI have used sklearn for Cross-validation and want to do a more visual information with the values of each model.\\n\\nThe problem is, I can't only get the name of the templates.\\nInstead, the parameters always come altogether. How can I only retrieve the name of the models without its parameters?\\nOr does it mean that I have to create an external list for the names?\\n\\nhere I have a piece of code:\\n\\nfor model in models:\\n   scores = cross_val_score(model, X, y, cv=5)\\n   print(f'Name model: {model} , Mean score: {scores.mean()}')\\nBut I also obtain the parameters:\\n\\nName model: LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False), Mean score: 0.8066782865537986\\nIn fact I want to get the information this way:\\n\\nName Model: LinearRegression, Mean Score: 0.8066782865537986\\nAny ideas to do that? Thanks!\\n\\nA:\\n\\n<code>\\nimport numpy as np\\nimport pandas as pd\\nfrom sklearn.linear_model import LinearRegression\\nmodel = LinearRegression()\\n</code>\\nmodel_name = ... # 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":[]}