I’m putting provider on this list for legacy reasons. Provider was the way to go for a while. It was simple, it was clea

These must-have tools for .NET developers will set you free from monotonous tasks and optimize your performance. Indeed, you don’t have to install all these tools at once, you can adjust them according to your project needs.,These must-have tools for .NET developers will set you free from monotonous tasks and optimize your performance. Indeed, you don’t have to install all these tools at once, you can adjust them according to your project needs.,SHAP (SHapley Additive exPlanations) is a game-theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions. (source: https://github.com/slundberg/shap),Less boilerplate than Provider: RiverPod does a great job trimming the fat of Provider, allowing you to just register a single top level store and then be done, rather than having to provide each provider individually. (Some of you might be wincing at the thought of everything in one place — Don’t worry! You can scope your pods if necessary.),Postman, the collaboration platform for API development, is an HTTP client used for creating APIs faster. Postman comes with wide support for all APIs and Schemas, including the possibility to make REST, SOAP, or plain HTTP calls. Collaboration platform allows inspecting even the largest responses. Developers can also easily run API tests there. The tool has a free version and Team, Business, or Enterprise editions.,No dependency on BuildContext: This is also a great one, for reasons mentioned already. Sometimes, you just can’t get the BuildContext where you need it. This might be the onAppClose listener, that you set up right in the main function.,And this is the point where things get subjective: I find this code unpleasant. Again I’m not talking about the technical attributes of this code. When I say it is ugly, I’m trying to express a set of feelings it evokes in me, which are heavily based on my own experience of the world in general and programming specifically. It is from this perspective that I’ll move on to say that the first problem that screams to me is JSX.,There’s no need for data cleaning — all data types are numeric, and there are no missing data. The train/test split is the next step. The column quality is the target variable, and it can be either good or bad. To get the same split, please set the value of random_state to 42:,Think of Provider as InheretedWidget, with less boilerplate. Infact, Provider is built ontop of InheretedWidget. It just reduces the amount of code you have to write, which is awesome.,Today you’ll learn how to explain machine learning models to the general population. We’ll use three different plots for interpretation — one for a single prediction, one for a single variable, and one for the entire dataset.,Nobody likes a black-box model. With sophisticated algorithms and a fair amount of data preparation, building good models is easy, but what’s going on inside? That’s where Explainable AI and SHAP come into place.,on RiverPod’s website, they call themselves “Provider, but different.” which couldn’t ring more true. Provider, even if it cuts out a lot of boilerplate, still has some that can be reduced further. Also, Provider depends on BuildContext — Which, I think in a lot of cases is really great (it forces you to use the widget tree) but sometimes, like app lifecycle, it’s just not practical to get BuildContext where you need it.,If your app is already using Provider, you’re 100% good to keep using it. It’s a very good package for what it does and there’s no reason to do a full migration to another solution.,Chocolatey is empowered by NuGet and PowerShell technology. Created by Microsoft, NuGet is a framework developed for the purposes of bundling code into “packages.” Besides NuGet Chocolatey uses PowerShell (a cross-platform task automation and configuration management framework) to add some functionality that helps to install and update packages.,But what are Shapely values? Put simply, they are measures of contributions each predictor (feature) has in a machine learning model. This is the least fancy definition on the web, guaranteed, but I reckon it’s easy enough to understand.