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21 Δεκ 2017 · The more reliable and way more flexible way is to do the prediction in Python using sklearn and communicate with your C# program via files or (better) a web service. Olivier Grisel (one of the sklearn authors) concisely describes your options in this post.
SciSharp provides ports and bindings to cutting edge Machine Learning frameworks like TensorFlow, Keras, PyTorch, Numpy and many more in .NET Core.
4 Ιουλ 2020 · In this article, we’ll look at a better way to bridge the technology gap between Data Scientists and App Developers using the ONNX Model format and the ONNX Runtime. Specifically, we’ll show how you can build and train a model using Sci-kit Learn, then use that same model to perform real-time inference in a .NET Core Web API.
Applications: Transforming input data such as text for use with machine learning algorithms. Algorithms: Preprocessing, feature extraction, and more...
12 Απρ 2024 · Scikit-learn is an open-source machine learning library that provides simple and efficient tools for data analysis and modeling. It is built on NumPy, SciPy, and Matplotlib, making it a powerful tool for tasks like classification, regression, clustering, and dimensionality reduction.
7 Μαΐ 2021 · Scikit-learn is an open source Python library that implements a range of machine learning, preprocessing, cross-validation and visualization algorithms using a unified interface. Your data needs to be numeric and stored as NumPy arrays or SciPy sparse matrices.
5 Απρ 2018 · How to predict classification or regression outcomes with scikit-learn models in Python. Once you choose and fit a final machine learning model in scikit-learn, you can use it to make predictions on new data instances.
