compas-analysis | Data and analysis for 'Machine Bias
kandi X-RAY | compas-analysis Summary
kandi X-RAY | compas-analysis Summary
Didn't Reoffend ||| Reoffended | | | +---------+. This repository contains a Jupyter notebook and data for the ProPublica story "Machine Bias.". Notebook (you'll probably want to follow along in the methodology): Main Dataset: compas.db - a sqlite3 database containing criminal history, jail and prison time, demographics and COMPAS risk scores for defendants from Broward County. Other files as needed for the analysis.
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QUESTION
I am working in Python with the compas-scores dataset of 47 columns and 11757 rows (https://github.com/propublica/compas-analysis).
I've noticed that when I am calling the dataset, it doesn't shows me all the columns. Instead I have a column in the middle with three dots [...] (see the example in the screenshot). The import statement of pandas was: import pandas as pd
Is there a way to show all the 47 columns?
I would apreciate your help.
SOLUTION:
I had to maximize all the columns adding one piece of code. This solution has worked for me:
pd.options.display.max_columns = None
ANSWER
Answered 2021-Mar-29 at 19:56assuming your import statement is import pandas as pd
I'd suggest trying one of the below:
QUESTION
I am trying to predict the probability of two_year_recid
by estimating a logit regression (with no penalty) that includes a flexible list of controls excluding decile_score
and race_factor
, but I keep getting an error saying
ANSWER
Answered 2020-Dec-09 at 17:35When you added the step step_dummy(all_nominal())
, that selected your outcome two_year_recid
and turned it into a dummy variable, because it is a nominal variable. Be sure to say you do not want to select it, either by adding it explicitly via -two_year_recid
or by using -all_outcomes()
. Then your model will fit and predict:
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