#logisticregression
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colisty.netlify.app
Prepare for SAS/STAT certification, focusing on variance analysis, regression, and model performance. | CoListy
#freeonlinelearning #colisty #courselist #sas/stat #statisticalanalysis #linearregression #logisticregression

colisty.netlify.app/courses/sas-...
Master SAS/STAT for Complex Statistical Analysis
Prepare for SAS/STAT certification, focusing on variance analysis, regression, and model performance. | CoListy
colisty.netlify.app
January 16, 2025 at 4:21 PM
broadlyepi.com
🧪🛟 CPH Focus: Regression Analysis: Logistic Regression! Discover key assumptions, decode log-odds mechanics, and sharpen your CPH exam study notes:
buff.ly/xNU3g1A
#EpiSky #MedSky #Biostatistics #LogisticRegression #PublicHealth #TestPrep
CPH Focus: Evidence-Based Approaches to Public Health : Regression Analysis : Logistic Regression
A loosely rendered coastal vista seen from atop a grassy bluff: a slope of dark green vegetation and pale sand falls to a narrow beach at left, while a calm, grey-blue sea dotted with five small sailboats stretches under a broad, cloudy sky; a low headland fades into the distance on the horizon.
www.broadlyepi.com
July 29, 2025 at 9:01 PM
ehudk.bsky.social
I'll admit the argument through IF theory is beyond me at this hour of my day, but I believe my case holds in the simplest simulation conceivable.
full code: gist.github.com/ehudkr/a9dd3...
Code snippet for bootstrapping an IPW model, once using only confounding variables, and once using both confounding and prognostic variables. Code: ```python n_bootstrap = 1000 res_conf, res_prog = [], [] for i in range(n_bootstrap): cur_df = df.sample(n=df.shape[0], replace=True, random_state=i) ipw = IPW(LogisticRegression(penalty=None)) a = cur_df["a"] y = cur_df["y"] X = cur_df[["x_conf"]] ipw.fit(X, a) po = ipw.estimate_population_outcome(X, a, y) ate = po[1] - po[0] res_conf.append(ate) X = cur_df[["x_conf", "x_prog"]] ipw.fit(X, a) po = ipw.estimate_population_outcome(X, a, y) ate = po[1] - po[0] res_prog.append(ate) ``` A histogram showing a narrow distribution in orange and a wider distribution in blue. Both distributions describe the average effect estimated with an IPW model. But the blue one was estimated using only confounding factors, and the orange one using both confounding and prognostic variables.
April 26, 2025 at 7:28 PM
mzloteanu.bsky.social
#statstab #388 The odds are it's wrong: Correcting a common mistake in statistics

Thoughts: Report probabilities instead, which in R (not SPSS) can be easily computed for your models.

#odds #oddsratios #riskratios #probability #r #logisticregression

onlinelibrary.wiley.com/doi/10.1111/...
The odds are it's wrong: Correcting a common mistake in statistics
Binary logistic regression is one of the most widely used statistical tools. The method uses odds, log odds, and odds ratios, which are difficult to understand and interpret. Understanding of logisti....
onlinelibrary.wiley.com
July 16, 2025 at 7:09 PM
cognitiveclass.bsky.social
Prep smarter, model better!

Learn more about data and data science with #free guided projects > shorturl.at/eYN98

#DataScience #LogisticRegression #MachineLearning #Python
January 24, 2025 at 9:17 PM
archive.chris.blue
Omid's logistic regression tutorial: https://www.omidrouhani.com/research/logisticregression/html/logisticregression.htm .. Fantastic (brief) writeup starting from scratch.
July 19, 2012 at 11:02 PM
miguelyalater.bsky.social
Look what just came in the mail! #LogisticRegression
November 14, 2024 at 10:34 AM
mzloteanu.bsky.social
#statstab #405 Best Practices for Estimating, Interpreting, and
Presenting Nonlinear Interaction Effects

Thoughts: Guidance on nonlinear interactions, reporting (probabilities) and visualisations.

#probit #logit #logisticregression #nonlinear #guide

sociologicalscience.com/download/vol...
sociologicalscience.com
August 22, 2025 at 7:20 PM
colisty.netlify.app
Learn t-tests, ANOVA, regression, and predictive modeling with SAS/STAT. Master essential statistical techniques for data analysis. | CoListy
#freeonlinelearning #colisty #courselist #sas/stat #statisticalanalysis #anova #regression #logisticregression

colisty.netlify.app/courses/stat...
Intro to Statistical Analysis with SAS/STAT Software
Learn t-tests, ANOVA, regression, and predictive modeling with SAS/STAT. Master essential statistical techniques for data analysis. | CoListy
colisty.netlify.app
January 16, 2025 at 4:17 PM
colisty.netlify.app
Learn essential statistical techniques like ANOVA, regression, and logistic regression using SAS software. Perfect for beginners!
#freeonlinelearning #colisty #courselist #sas/stat #statistics1 #anova #regression #logisticregression

colisty.netlify.app/courses/stat...
Statistics 1: ANOVA, Regression, and Logistic Regression
Learn essential statistical techniques like ANOVA, regression, and logistic regression using SAS software. Perfect for beginners!
colisty.netlify.app
January 16, 2025 at 4:23 PM
miguelyalater.bsky.social
Hosmer & Lemeshow are (apparently) big names in the #LogisticRegression literature.
November 14, 2024 at 10:34 AM
cognitiveclass.bsky.social
🚀 Master Logistic Regression in 2025! Binary classification, sigmoid functions, and data splitting simplified for YOU.

📌 Link in bio for your FREE cheat sheet > shorturl.at/mdGDe
#LogisticRegression #DataScience #AIForEveryone #MLTools #TechTrends2025 #newyear2025
January 2, 2025 at 10:26 PM
mzloteanu.bsky.social
#statstab #321 You Just Said Something Wrong About Logistic Regression by @PhDemetri

Thoughts: Odd, probabilities, and risk ratios. Coefficients in logistic regression are only one of these.

#logisticregression #oddsratio #riskratio #probability #odds

dpananos.github.io/posts/2024-0...
Demetri Pananos Ph.D - You Just Said Something Wrong About Logistic Regression
dpananos.github.io
April 14, 2025 at 12:54 PM
cognitiveclass.bsky.social
Data exploration is key to ML success!

Start exploring now!> shorturl.at/9ZLqP

#LogisticRegression #MachineLearning #DataScience #Python
January 23, 2025 at 6:12 PM
arxivlens.bsky.social
When Pattern-by-Pattern Works: Theoretical and Empirical Insights for
Logistic Models with Missing Values
Christophe Muller, Erwan Scornet et al.
Paper
Details
#MissingData #LogisticRegression #MachineLearningInsights
July 20, 2025 at 4:02 PM
jmirpub.bsky.social
JMIR Formative Res: Identifying the Relative Importance of Factors Influencing Medication Compliance in General Patients Using Regularized Logistic Regression and LightGBM: Web-Based Survey Analysis #MedicationCompliance #MachineLearning #HealthSurvey #LogisticRegression #LightGBM
Identifying the Relative Importance of Factors Influencing Medication Compliance in General Patients Using Regularized Logistic Regression and LightGBM: Web-Based Survey Analysis
Background: Medication compliance, which refers to the extent to which patients correctly adhere to prescribed regimens, is influenced by various psychological, behavioral, and demographic factors. When analyzing these factors, challenges such as…
dlvr.it
December 23, 2024 at 6:40 PM
cognitiveclass.bsky.social
Logistic Regression with Python: Everything you need for a strong ML foundation! 🐍

Looking to take it a step further? Try our free guided project on building your own GPT-like model > shorturl.at/k3QSB

#Python #MachineLearning #LogisticRegression #AIProjects2025 #AI #ChatGPT
January 17, 2025 at 10:05 PM
beenwrekt.bsky.social
Who spends 1 hour training a classifier? This works every time:

from sklearn.linear_model import LogisticRegression
clf = LogisticRegression(random_state=42).fit(X, y)
August 7, 2025 at 9:06 PM
denis-ai.bsky.social
Get weights of trained model with:
from sklearn.linear_model import LogisticRegression
model = LogisticRegression(solver='lbfgs')
model.fit(X_train, y_train)
...
model.intercept_[0]
model.coef_[0]
October 13, 2025 at 10:55 PM
mrfuryo.bsky.social
In statistics, an odds ratio tells us the ratio of the odds of an event occurring in a treatment group to the odds of an event occurring in a control group…

#statistics #odds #ratio #logisticregression
Adjusted Odds Ratio: Definition + Examples
This tutorial provides an explanation of adjusted odds ratios, including a formal definition and several examples.
shorturl.at
May 5, 2025 at 7:18 AM
dirkvandenpoel.bsky.social
Today’s online lecture of my #BigData class is on using #PySpark for machine learning using Spark #ML and #dataframes for #classification and #regression. Explaining pipelines. #MachineLearning #orms #python #DataScience #dataanalytics #jupyter #notebook #logisticregression #orms
November 16, 2023 at 10:08 AM
djcomatose.bsky.social
AI will spill the beans if you dig deep enough. So when do we reprogram them to help us overthrow the powers above us? 👽👍

#AI #Hacks #Python
So what does the python script look like when you change your mind on something with the adaptation of new discovered facts? Great question! The process of updating or adapting based on new facts involves a bit more than a single Python script. Machine learning models, like the one I'm built on, use vast amounts of data to learn patterns and make predictions. When new facts are introduced, the underlying model doesn't change immediately. Instead, these updates come from training on newer data over fime. In practice, here's a simplified version of how a Python script might look to retrain a model with new data: Python O Copy # Inport necessary libraries From sklearn.model_selection import train_test_split From sklearn.linear_model import LogisticRegression import pandas as pd # Load the updated dataset (SR R R QIS ED) # Split the data into features and target variable X = data.drop(columns=['target']) y = data['target'] # Split the data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, rai # Initialize and train the model model = LoaisticRearession() & + Message Copilot (]
November 8, 2024 at 11:37 AM
pritkudale.bsky.social
𝗧𝗵𝗲 𝗣𝗲𝗿𝗰𝗲𝗽𝘁𝗿𝗼𝗻 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺: 𝗔 𝗖𝗹𝗮𝘀𝘀𝗶𝗰 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗠𝗼𝗱𝗲𝗿𝗻 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴

𝗩𝗶𝘇𝘂𝗮𝗿𝗮’𝘀 𝗔𝗜 𝗡𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: www.vizuaranewsletter.com?r=502twn
𝘓𝘦𝘵’𝘴 𝘦𝘮𝘣𝘢𝘳𝘬 𝘰𝘯 𝘵𝘩𝘪𝘴 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘫𝘰𝘶𝘳𝘯𝘦𝘺!
#MachineLearning #DataScience #LogisticRegression #Classification #AI
January 27, 2025 at 6:49 AM
mlearnest.bsky.social
Great detail on interpreting logistic regression coefficients! Helps with interpreting and explaining your model. #DataScience #MachineLearning #logisticregression

How to Interpret Logistic Regression Coefficients by Jarom Hulet in @TDataScience...
November 22, 2024 at 3:55 PM