Boosting Your Method to SuccessImagine running a relay race. Each runner improves upon the previous one’s performance, and together, they win the race. That’s how these algorithms work — every latest model compensates for...
Hey there! I’m Ana, a knowledge enthusiast and a machine learning apprentice. Welcome to my first post on Medium, where I’ll be sharing my journey and insights into the exciting world of knowledge evaluation...
With these gradients, we will use (stochastic) gradient descent to reduce the loss function on the given training set.You might be given a set of images and you must classify them into dogs/cats and...
Looking under the hood on the matrix operations behind linear regression11 min read·12 hours agoFitted ValuesWe've got now used some linear algebra to seek out the best-fitting parameters for a straightforward linear regression model....
Principally, all metrics exploded in size, which is intuitively consistent. That will not be the case for sMAPE, which stayed the identical between each cases.I highly encourage you to mess around with such toy...
Deep Dive into Multiple Linear Regression with Examples in PythonLet’s reduce the training rate from 0.01 to 0.001 by changing the parameter eta0 of the SGDRegressor:pipeline.set_params(reg__eta0=0.001)Let’s refit the pipeline to the training set and...
How the common-or-garden prediction method shows us the method to Generative AIThere are many writings about Generative AI. There are essays dedicated to its applications, ethical and moral issues, and its risk to human...