marr75
/ vectorized_f1_calc.py
Created
January 6, 2023 15:54
Quick vectorized approach to calculating metrics at every potential breakpoint for a classification problem. Nice way to visualize precision, recall, and f1 in continuous graphs if you're introducing unsupervised machine learning or talking about very shallow decision trees.
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| def evaluate_threshold_binary_classification(x: pandas.Series, y: pandas.Series, reverse=False): | |
| """ Calculate quality metrics such as True Positive, True Negative, False Positive, False Negative, Precision, Recall, and F1 | |
| for all possible thresholds of a metric x being used to predict a classification against a binary class, y. | |
| x: the independent variable we will use as a predictor, a continuos variable | |
| y: the dependent variable representing the class we are predicting, 0/1 or bool | |
| reverse: whether the x and y series have an inverse relationship | |
| """ | |
| predictor = pd.DataFrame( | |
| { | |
| 'discriminant': x * (1 and reverse or -1), |
marr75
/ finished-digital-clock-in-javascript.markdown
Created
January 16, 2021 16:51
Finished: Digital Clock In JavaScript
marr75
/ missouri-oz-regions.geojson
Created
October 1, 2019 15:41
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marr75
/ map.geojson
Last active
December 5, 2016 19:58
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marr75
/ postgres_stats.sql
Last active
March 2, 2023 03:00
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| with table_stats as ( | |
| select psut.relname, | |
| psut.n_live_tup, | |
| 1.0 * psut.idx_scan / greatest(1, psut.seq_scan + psut.idx_scan) as index_use_ratio | |
| from pg_stat_user_tables psut | |
| order by psut.n_live_tup desc | |
| ), | |
| table_io as ( | |
| select psiut.relname, | |
| sum(psiut.heap_blks_read) as table_page_read, |