Gorillaz ML

Features

Regression

• Linear Regression: Ordinary Least Squares, Ridge, Lasso, ElasticNet, Polynomial, Bayesian, and Robust regression models.
• Customizable: Offers tools for handling regularization and feature transformations.

Classification

• Decision Tree Classifier: Highly configurable with parallel tree building and support for multiclass classification.
• Linear Classifiers: Logistic Regression and Support Vector Machines (SVM) with gradient-based training.
• Naive Bayes: Multinomial Naive Bayes for probabilistic classification.
• k-Nearest Neighbors (KNN): KNN classifier with Euclidean distance for classification tasks.

Utilities

• Model Evaluation: Includes metrics like accuracy, confusion matrix, precision, recall, and F1 score.
• Model Persistence: Save and load models using a simple API with Go’s gob encoding.

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Installation

To use Gorillaz, install it using Go modules:

go get github.com/yourusername/gorillaz

Example

package main

import (
	"fmt"
	"github.com/yourusername/gorillaz"
)

func main() {
	X := [][]float64{{1, 2}, {3, 4}, {5, 6}}
	Y := [][]float64{{1}, {2}, {3}}

	model := gorillaz.LinearRegression{}
	err := model.FitOLS(X, Y)
	if err != nil {
		fmt.Println("Error training model:", err)
		return
	}

	predictions, err := model.Predict([][]float64{{7, 8}})
	if err != nil {
		fmt.Println("Error making predictions:", err)
		return
	}

	fmt.Println("Predictions:", predictions)
}