JS Bin VS scikit-learn

JS Bin is one of the useful JavaScript debugging tools designed for developers working with HTML, CSS, and JavaScript. It gives them the opportunity to test and debug their code snippets in a real-world setting. The fact that this tool is open-source is fantastic.

If I paste both in jsbin.com, the both show all content on 1 line.

A running example in https://jsbin.com/ might be easier to help with

JSBin : Front end playground, Output is not framed, so it allows you to share those snippets that will break inside an iframe.

> What if you want introduce some diagramming or Latex rendering in your blog ?

Then you are starting to step beyond what I was referring to as “a simple blog” (text, formatting, maybe some pictures or SVG).

Though there are options that don't require a CI/CD pipeline. Depending on what Latext you need MathJax may do the trick (just include the JS in your standard page header, and drop the Latex code straight in inline where needed as per their standard example: https://jsbin.com/?html,output. There are similar charting options like mermaid too. I don't know of inline charting options off the top of my head but I expect several exist.

Post your code on codesandbox.io or jsitor.com or jsbin.com or jsfiddle.net or whatever other site you prefer. Then we can look at it and see what is wrong. Otherwise, it's like showing us a picture of your cat and asking whether we think you might be allergic to it.

Important note: do not perform tests with WeakRefs in dev tools. Your objects won't be GCed in it due to DevTools specific behaviour. Instead, use jsbin, codesandbox, runkit or perform tests in Node.js. Localhost is okay when you open files using http:// protocol, as file:// also has specific behaviour.

https://jsbin.com/ (Type in the code; press go)

Online Courses: Coursera: "Machine Learning" by Andrew Ng edX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By understanding the core concepts of logistic regression, its limitations, and exploring further resources, you'll be well-equipped to navigate the exciting world of machine learning!

Thank you for your interest. There are some interesting examples in the SWE-bench-lite benchmark which are resolved by AutoCodeRover:

- From sympy: https://github.com/sympy/sympy/issues/13643. AutoCodeRover's patch for it: https://github.com/nus-apr/auto-code-rover/blob/main/results...

- Another one from scikit-learn: https://github.com/scikit-learn/scikit-learn/issues/13070. AutoCodeRover's patch (https://github.com/nus-apr/auto-code-rover/blob/main/results...) modified a few lines below (compared to the developer patch) and wrote a different comment.

There are more examples in the results directory (https://github.com/nus-apr/auto-code-rover/tree/main/results).

sklearn is adding support through the dataframe interchange protocol (https://github.com/scikit-learn/scikit-learn/issues/25896). scipy, as far as I know, doesn't explicitly support dataframes (it just happens to work when you wrap a Series in `np.array` or `np.asarray`). I don't know about PyTorch but in general you can convert to numpy.

Wow, from the upvotes on this comment, it really seems like a lot of people think that this is the correct behavior! I have to say I disagree, but if that's what you think, don't just sit there upvoting comments on Reddit; instead go to this PR and tell the Scikit-Learn maintainers not to "fix" this "bug", which they are currently planning to do!

I was in a similar boat to yours - Worked in data science and since then have made a move to data engineering and software engineering for ML services.

I would recommend you look into the Design Patterns book by the Gang of Four. I found it particularly helpful to make extensible code that doesn't break specially with abstract classes, builders and factories. I would also recommend looking into the book The Object Oriented Thought Process to understand why traditional OOP is build the way it is.

You can also look into the source code of popular data science libraries such as sklearn (https://github.com/scikit-learn/scikit-learn/tree/main/sklea...) and see how a lot of them have Base classes to define shared functionality between object of the same nature.

As others mentioned, I would also encourage you to try and implement design patterns in your everyday work - maybe you can make a Factory to load models or preprocessors that follow the same Abstract class?

It looks like you've been the victim of some misinformation. As Dr_Birdbrain said, an SVM is a convex problem with unique global optimum. sklearn.SVC relies on libsvm which initializes the weights to 0 [0]. The random state is only used to shuffle the data to make probability estimates with Platt scaling [1]. Of the random_state parameter, the sklearn documentation for SVC [2] says

Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when probability is False. Pass an int for reproducible output across multiple function calls. See Glossary.

[0] https://github.com/scikit-learn/scikit-learn/blob/2a2772a87b...

[1] https://en.wikipedia.org/wiki/Platt_scaling

[2] https://scikit-learn.org/stable/modules/generated/sklearn.sv...

Scikit-learn Documentation

Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy.