Train and visualize a Linear SVM with adjustable parameters
Upload ML model to Hugging Face Hub
Evaluate open LLMs in the languages of LATAM and Spain.
Launch web-based model application
Optimize and train foundation models using IBM's FMS
Evaluate RAG systems with visual analytics
Create and manage ML pipelines with ZenML Dashboard
Browse and filter machine learning models by category and modality
Benchmark LLMs in accuracy and translation across languages
View and submit LLM benchmark evaluations
Find recent high-liked Hugging Face models
Track, rank and evaluate open LLMs and chatbots
Browse and submit model evaluations in LLM benchmarks
Support Vectors LinearSVC is a Linear Support Vector Machine (SVM) implementation designed for classification and regression tasks. It is a powerful tool for training and visualizing SVM models with adjustable parameters. This model is particularly useful for understanding how SVMs work by allowing users to tweak various settings and observe the impact on the decision boundary.
sklearn and matplotlib for training and visualization.C (cost) and max_iter.fit() method to train the model on your dataset.What is a support vector?
A support vector is a data point that lies closest to the decision boundary and influences the model's prediction. These points are crucial for defining the separating hyperplane in SVM models.
Why should I use LinearSVC over other SVM implementations?
LinearSVC is particularly well-suited for linearly separable data and provides more flexibility in parameter tuning compared to other SVM variants. Its visualization capabilities also make it an excellent choice for educational and exploratory purposes.
How does LinearSVC handle non-linear data?
LinearSVC can handle non-linear data by using kernel tricks, where the data is implicitly mapped to a higher-dimensional space. However, for highly non-linear data, other SVM variants like SVC with a radial basis function (RBF) kernel may be more appropriate.