Last updated: 2026-05-31
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- The URL, title, and favicon of tabs you choose to save.
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| name: value-of-information | |
| description: > | |
| Calculate the Expected Value of Information (EVI / EVPI) to determine whether | |
| gathering more data before a decision is worth the cost. Use this skill | |
| whenever the user is deciding whether to do research, run a study, conduct a | |
| survey, buy a report, or collect more data before acting. Trigger on: "is this | |
| research worth it?", "should we do market research?", "is the survey worth | |
| buying?", any mention of EVPI, EVI, EOL, Expected Opportunity Loss, or Value | |
| of Information, or any cost-benefit question about information gathering. Also |
| import argparse | |
| import json | |
| import logging | |
| import os | |
| import re | |
| import shutil | |
| from concurrent.futures import ProcessPoolExecutor, as_completed | |
| from dataclasses import dataclass | |
| from datetime import datetime | |
| from typing import Any, Callable, Dict, List, Literal, Optional, Tuple |
| import pandas as pd | |
| from sklearn.preprocessing import LabelEncoder,OneHotEncoder | |
| def decode_encode(colname): | |
| ''' | |
| (str) -> (DataFrame) | |
| Returns a Data Frame with the column given to it transformed into a One-hot encoded set of columns |
| import pandas as pd | |
| from scipy.stats import mode | |
| grouped_mod = df.pivot_table(values='The metric you need the mode for',columns='Category',aggfunc=mode) |