Search Results for "thinking"

...DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks. Much like machine learning libraries have done for prediction, DoWhy is a Python library that aims to spark causal thinking and analysis. DoWhy provides a wide variety of algorithms for effect estimation, causal structure learning, diagnosis of causal structures, root cause analysis, interventions and counterfactuals. DoWhy builds on two of the most powerful frameworks for causal inference: graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. ...

...Many of the questions cover fundamental machine learning topics such as linear models, decision trees, neural networks, and evaluation metrics. In addition to theoretical questions, the repository also includes practical interview topics related to coding challenges, SQL queries, and algorithmic thinking.

...I sometimes needed a tool sometimes, which I could use to fast create a machine learning prototype. Whether to build some proof of concept, create a fast draft model to prove a point or use auto ML. I find myself often stuck writing boilerplate code and thinking too much about where to start. Therefore, I decided to create this tool. igel is built on top of other ML frameworks. It provides a simple way to use machine learning without writing a single line of code. Igel is highly customizable, but only if you want to. Igel does not force you to customize anything. Besides default values, igel can use auto-ml features to figure out a model that can work great with your data.