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Xircuits Component Library for integrating scikit-learn models, datasets, and evaluation tools.
This library enables seamless integration of scikit-learn's machine learning models, datasets, and evaluation tools into Xircuits, streamlining data workflows, model training, and performance evaluation.
Before you begin, you will need the following:
- Python3.9+.
- Xircuits.
Initializes a RandomForestClassifier for high-accuracy classification tasks, using specified or default parameters.
Initializes a LogisticRegression model, widely used for binary classification and multiclass tasks using a one-vs-rest strategy.
Initializes an Support Vector Classifier (SVC), effective in high-dimensional spaces and suitable for cases with more features than samples.
Initializes a KNeighborsClassifier, an instance-based learning model that classifies data based on stored training instances without building a generalized model.
Initializes a DecisionTreeClassifier, a versatile model for classification and regression that uses a tree structure to make decisions through yes/no questions.
Initializes a GradientBoostingClassifier that builds models additively in stages, optimizing differentiable loss functions for improved accuracy.
Initializes a Support Vector Regression (SVR) model, applying Support Vector Machines (SVM) principles to regression with customizable kernels for handling complex datasets.
Initializes a MultinomialNB model, ideal for discrete features like word counts and effective for multi-class text classification.
Initializes a Ridge Regression model that mitigates overfitting by penalizing large coefficients, enhancing the robustness of linear regression.
Initializes a KMeans model, an unsupervised algorithm that partitions data into k clusters by assigning each point to the nearest cluster mean.
We have provided an example workflow to help you get started with the Sklearn component library. Give it a try and see how you can create custom Sklearn components for your applications.
Check out the TrainEvaluate workflow. This example uses Sklearn components to load the Iris dataset, split it into training and testing sets, and train a Random Forest model. It evaluates the model's performance with classification metrics, showcasing an end-to-end machine learning pipeline.
To use this component library, ensure that you have an existing Xircuits setup. You can then install the SKLearn library using the component library interface, or through the CLI using:
xircuits install sklearn
You can also do it manually by cloning and installing it:
# base Xircuits directory
git clone https://github.com/XpressAI/xai-sklearn xai_components/xai_sklearn
pip install -r xai_components/xai_sklearn/requirements.txt