{"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":"ba99a850-1ec5-5849-8ddc-a41bfef8c787","task_key":"default--test--827","task_revision_id":"1","upstream_id":"827","short_description":"I'm trying to solve some two classes classification problem. And I just use the…","config":"default","split":"test","body":"{\"library\":\"Sklearn\",\"prompt\":\"Problem:\\n\\nI'm trying to solve some two classes classification problem. And I just use the LinearSVC from sklearn library.\\nI know that this LinearSVC will output the predicted labels, and also the decision scores. But actually I want probability estimates to show the confidence in the labels. If I continue to use the same sklearn method, is it possible to use a logistic function to convert the decision scores to probabilities?\\n\\nimport sklearn\\nmodel=sklearn.svm.LinearSVC(penalty='l1',C=1)\\npredicted_test= model.predict(x_predict)\\npredicted_test_scores= model.decision_function(x_predict)\\nI want to check if it makes sense to obtain Probability estimates simply as [1 / (1 + exp(-x)) ] where x is the decision score.\\n\\nAnd I found that CalibratedClassifierCV(cv=5) seemed to be helpful to solve this problem.\\nCan anyone give some advice how to use this function? Thanks.\\nuse default arguments unless necessary\\n\\nA:\\n\\n<code>\\nimport numpy as np\\nimport pandas as pd\\nfrom sklearn import svm\\nX, y, x_predict = load_data()\\nassert type(X) == np.ndarray\\nassert type(y) == np.ndarray\\nassert type(x_predict) == np.ndarray\\nmodel = svm.LinearSVC()\\n</code>\\nproba = ... # 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":[]}