TraditionData’s Post

Interested in how to create ICE-Plots for model interpretation? In this video, I show how to use the ML-insights package to create ICE-plots, visualize the background data of the model, and even show how the model treats missing values! Check it out! https://lnkd.in/gWHi9wA3

Here’s an intriguing number coincidence: when you divide 987,654,321 by 123,456,789, the result is almost a perfect 8! Curious to discover more fascinating quirks of numbers? Dive deeper into the world of numbers by visiting my site: https://lnkd.in/dJ4BGZC4 #MathMagic #NumberFacts #CuriousNumbers #NumericalQuirks #MathematicalPuzzles #ArchimedesLab

#AgricultureMdpi – Editor's Choice Article 📜 Prediction of #Wheat Stripe Rust Occurrence with Time Series Sentinel-2 Images 🖋️ by Chao Ruan et al. 👇 Click to read the full article: https://lnkd.in/gnRZqhJH #striperust #prediction #timeseries #remotesensing

To find the area of the square inside the right triangle, we use the relationship between the dimensions of the right triangle and the square. The triangle has side lengths 3, 4, and 5, confirming it's a Pythagorean triple. The base and height are 3 and 4, and the hypotenuse is 5. Let the side of the square be \( s \). The formula to find \( s \) for a square inscribed in a right triangle is: \( s = \frac{ab}{a + b + c} \) Where \( a = 3 \), \( b = 4 \), and \( c = 5 \). So: \( s = \frac{3 \times 4}{3 + 4 + 5} = \frac{12}{12} = 1 \). The area of the square is: \( \text{Area} = s^2 = 1^2 = 1 \). The area of the square is 1 square unit. #MathPuzzle #RightTriangle #Geometry

Prodigy infotech ML task 3 - This task involves using a pre-trained VGG16 model to extract features from images of cats and dogs. These features are then used to train a Support Vector Machine (SVM) classifier to distinguish between the two classes. The process includes data preprocessing, feature extraction, model training, evaluation, and prediction. #ProdigyInfoTech #MachineLearning #SVM

TidyX Episode 176: Extracting all predictions from a Random Forest, Building Uncertainty Intervals, and Creating a Player Compare Tool Ellis Hughes & I show how to extract all predictions from a random forest model, build a distribution & make a player compare function https://lnkd.in/gSQifwG9

Day 78/100 of #100DaysOfAI Started working on the next project, A dog and cat image classifier. We will be using an image generator to generate multiple image variations for each in the dataset. #MachineLearning #100DaysOfCode