#logisticregression 搜尋結果

🚀 Day 91 of #100DaysOfML ✅ Dove into #LogisticRegression assumptions, regularization. Key: binary outcome, independent observations, linearity to log-odd, low multicollinearity, large sample. L1/L2/Elastic Net prevent overfitting by penalizing coefficients. #ML #AI #DataScience

🚀 Day 90 of #100DaysOfML ✅ Dove into #LogisticRegression & MLE 🔍. Sigmoid models probabilities; training maximizes likelihood (minimizes log loss) using G Descent. MLE estimates parameters, links to loss functions, aids interpretability & model comparison. #ML #AI #DataScience

🤔 Logistic Regression isn’t about regression—it’s for classification. It predicts probabilities between 0 and 1. #ML #LogisticRegression #AI

Just built a logistic regression model to predict if a user will purchase a product after seeing an ad. By turning salary into binned categories, I was able to capture non-linear patterns and boost test accuracy from ~86% to ~95% github.com/Ravi0529/purch… #LogisticRegression

Binary Logistic Regression in SPSS: The Complete Point-and-Click Guide theacademicpapers.co.uk/blog/2025/09/2… #BinaryLogisticRegression #BinaryRegression #LogisticRegression #SPSS

An Asymmetric Ensemble Method for Determining the Importance of Individual Factors of a Univariate Problem ✏️ Jelena Mišić et al. 🔗 brnw.ch/21wVY65 Viewed: 1451; Cited: 4 #mdpisymmetry #binaryclassification #logisticregression #featureselection

🔍 Took a closer look at the Sigmoid function today: ✨ Key insights: When z → +∞, sigmoid → 1 When z → -∞, sigmoid → 0 At z = 0, sigmoid = 0.5 (the decision boundary) #MachineLearning #LogisticRegression #BuildInPublic

📘 Learning Report #5 – Logistic Regression & Decision Trees Deep Levelling up classification models! Covered: #MachineLearning #LogisticRegression #DecisionTree #Python #ML #AI

📚 Today I explored Logistic Regression — from hypothesis function to the log loss function. Loved seeing how it shifts from squared error to cross-entropy for better optimization ! #MachineLearning #LogisticRegression #DataScience #100DaysOfML

I have learned this in my basic econometrics class in Sem 1 of PG in econ. But unfortunately i have forgotten the calculations i have to revise it .. #LogisticRegression #Econometrics #Economics

One logistics firm used AI to automate route planning and delivery schedules, cutting fuel costs and boosting on-time deliveries. AI transforms complexity into efficiency. Discover AI solutions in logistics: algoworks.com #logisticregression #Aiinfra #DigitalTrust…

🚀 Leveling up my #MachineLearning skills! Just mastered optimizing #LogisticRegression 📊 🔹 Parameters tuned: C, penalty, solver, max_iter 🔹 Result: Higher accuracy & less overfitting Tuning = the secret sauce for better predictions 🧠✨ #AI #100DaysOfCode #ML

📚 Just learned Logistic Regression from scratch! ✅Understood the Sigmoid function ✅Implemented Log Loss (Binary Cross Entropy) ✅Trained using Gradient Descent It’s amazing how ML models predict probabilities from just math #MachineLearning #100DaysOfML #LogisticRegression⌨️

👉👉 Spatial Prediction of #Landslide Susceptibility Using #LogisticRegression (LR), #FunctionalTrees (FTs), and #RandomSubspaceFunctionalTrees (RSFTs) for Pengyang County, China ✍️ Hui Shang et al. 📎 brnw.ch/21wNu4N

📌 Today I practiced Polynomial Logistic Regression and learned how it improves classification on non-linear data. Check out my code here 👉 github.com/Its-Vikas-xd/M… #MachineLearning #LogisticRegression #AI

Should You Click ‘Yes’ or ‘No’? Predicting Choices with Logistic Regression #DataScience #logisticregression #medium medium.com/p/should-you-c…

Data exploration = the foundation of every ML model!🛠️ #MachineLearning #DataScience #LogisticRegression #Python

Day 42 of #LearningWithLeapfrog! 🎉 Explored logistic regression and data visualization. Plotted test scores, computed cost and gradient, implemented gradient descent, and visualized the decision boundary. #LogisticRegression #MachineLearning #60DaysofLearning2024 #LPPD42

Just built a logistic regression model to predict if a user will purchase a product after seeing an ad. By turning salary into binned categories, I was able to capture non-linear patterns and boost test accuracy from ~86% to ~95% github.com/Ravi0529/purch… #LogisticRegression

Can you predict marketing outcomes? 🤔 I used logistic regression in R to model website conversions by traffic source. 📊📈 Read my article 👉 blog.marketingdatascience.ai/what-happens-n… 🐝 @gtomsanalytics @GATechScheller #RStats #MarketingAnalytics #LogisticRegression

🚀 Implemented Logistic Regression from scratch in JavaScript — no libraries, just pure math and logic! 🧠 Great hands-on learning experience to understand how ML models actually work under the hood. #ml #machinelearning @python #logisticregression @ChaiCodeHQ @Hiteshdotcom

🚀 Day 90 of #100DaysOfML ✅ Dove into #LogisticRegression & MLE 🔍. Sigmoid models probabilities; training maximizes likelihood (minimizes log loss) using G Descent. MLE estimates parameters, links to loss functions, aids interpretability & model comparison. #ML #AI #DataScience

Preprocessing data = turning chaos into clarity!🚀Here is pt 3 of the LR with #Python Cheat Sheet. #MachineLearning #LogisticRegression #DataScience

🔍 The #SigmoidFunction in #LogisticRegression maps inputs to a 0-1 range with an S-shaped curve. 🌀 It converts model outputs into probabilities, classifying results as 1 (≥0.5) or 0 (<0.5). Ideal for binary classification! 📈 #MachineLearning #AI #DataScience

Day 2 of my summer fundamentals series: Built Logistic Regression from scratch using just NumPy. No scikit-learn, no TensorFlow — just math, sigmoid functions, and gradient descent. Turning probabilities into predictions, one step at a time. #MLfromScratch #LogisticRegression

ML from Scratch — Day 17 (19/05) [~1.5 hrs] - wrote logistic regression with the help of youtube videos - read more theory and implemented basics from scratch #ML #LogisticRegression #Python

🚀 #LogisticRegression: A binary classifier that predicts the probability of a class using an S-shaped curve (sigmoid). 🎯 Outputs range from 0 to 1: If the probability ≥ 0.5, it's classified as 1; otherwise, 0. 📊 #MachineLearning #AI #DataScience

Built a logistic regression model to predict if someone will buy based on age & salary. Got 92% chance of buying for age 41, salary 78k. Accuracy: 75% #machinelearning #python #logisticregression #100daysofcode

Logistic regression predicts the likelihood of an event using mathematical analysis of variables, yielding results between 0 and 1 for binary outcomes. #LogisticRegression #PredictiveModeling #DataScience #MachineLearning #Statistics #BinaryOutcomes #PredictiveAnalytics #Data

🚀 Day 91 of #100DaysOfML ✅ Dove into #LogisticRegression assumptions, regularization. Key: binary outcome, independent observations, linearity to log-odd, low multicollinearity, large sample. L1/L2/Elastic Net prevent overfitting by penalizing coefficients. #ML #AI #DataScience