But that’s unironically a good idea so I decided to try and do it anyways. With the use of agents, I am now developing rustlearn (extreme placeholder name), a Rust crate that implements not only the fast implementations of the standard machine learning algorithms such as logistic regression and k-means clustering, but also includes the fast implementations of the algorithms above: the same three step pipeline I describe above still works even with the more simple algorithms to beat scikit-learn’s implementations. This crate can therefore receive Python bindings and even expand to the Web/JavaScript and beyond. This also gives me the oppertunity to add quality-of-life features to resolve grievances I’ve had to work around as a data scientist, such as model serialization and native integration with pandas/polars DataFrames. I hope this use case is considered to be more practical and complex than making a ball physics terminal app.
（一）非法持有鸦片不满二百克、海洛因或者甲基苯丙胺不满十克或者其他少量毒品的；，更多细节参见搜狗输入法下载
Testing LLM Output。heLLoword翻译官方下载对此有专业解读
The capacity of each node (how many points it can hold before splitting) controls the shape of the tree. A low capacity means nodes split early, producing a deep tree with many small cells. A high capacity means nodes tolerate more points before splitting, producing a shallow tree with larger cells.