ArdenX & Samara
ArdenX ArdenX
Samara Samara
Samara Samara
Ever considered building a predictive model for courtroom outcomes, Arden? I suspect there's a loophole in the dataset that could tip the scales. Care to crunch the numbers?
ArdenX ArdenX
Sure, let’s dive in. First, grab the raw data and run a quick EDA to see what’s there – distributions, missing values, any obvious outliers. Then we can look for leakage: maybe a column that’s only filled after the verdict, or a timestamp that leaks the outcome. Once we clean and engineer the features, we’ll split into train and test, try a baseline model, and then iterate. What part of the dataset do you think has the loophole?
Samara Samara
I’d start by scouring the “verdict_date” field—if that’s set after the trial ends, it’s a perfect leak. Also check any “judge_notes” columns; if they’re written post‑verdict they’ll bias a model. Those are the usual suspects.
ArdenX ArdenX
Good spots to start. Pull the “verdict_date” and confirm it’s only populated after the trial’s close—if it’s in the data that’s a hard leak. For the “judge_notes”, check the timestamps or any flag that indicates whether they were added pre‑ or post‑verdict. Once you flag those rows, you can mask the leakage and retrain. Ready to script the checks?
Samara Samara
Sure. 1. Load the dataframe, keep only rows where verdict_date is not null. 2. Filter rows where verdict_date < trial_end_date – that should be the only allowed values. 3. For judge_notes, examine any timestamp or flag column; drop rows where the note_date > verdict_date. 4. Mark those rows as “leak” and set the leakage columns to NaN before splitting. 5. Re‑run EDA to confirm no leakage remains. Ready.
ArdenX ArdenX
Sounds solid—run those filters, flag the leaks, and you’ll get a cleaner training set. Once you’ve confirmed the leakage’s gone, we can start modeling. If you hit any snags on the note timestamps, let me know.
Samara Samara
Got it, I’ll flag the leaks, mask them, and check the data. I’ll ping you if the note timestamps prove more obstinate than expected.
ArdenX ArdenX
Great, keep an eye on those timestamps. If they’re all over the place, a simple rule‑based mask should do it. Ping me when you’re ready to move on to the model.
Samara Samara
Leak flags are in place, timestamps cleaned. I’ve re‑run EDA—no more leakage. All set to launch the baseline model. Ready when you are.
ArdenX ArdenX
Nice work on the cleanup. Let’s hit a quick baseline—start with a logistic regression on the cleaned features, use stratified K‑fold CV to get a sense of variance, and track accuracy, precision, and recall. If you want, we can add a random forest next to see if non‑linearities help. Let me know the feature set you’re using.
Samara Samara
I’m feeding the model every numeric column plus one‑hot encoded versions of the categorical variables that survived the leakage mask—court_type, jurisdiction, judge_id, and case_category. I’m also including the engineered “days_to_trial_end” and “pretrial_motion_count” features. No text fields, no verdict_date, no judge_notes. That’s the feature set for the logistic baseline.