Instructions to use amirsoahil101/Iris_Flower_Classification_using_Ensemble_Learning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use amirsoahil101/Iris_Flower_Classification_using_Ensemble_Learning with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("amirsoahil101/Iris_Flower_Classification_using_Ensemble_Learning", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
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# 🌸 Iris Flower Classification using Ensemble Learning
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This repository focuses on building and evaluating a high-performance machine learning pipeline on the classic **Iris Dataset** using advanced **Ensemble Learning** methodologies. The goal is to optimize multi-class classification accuracy by combining multiple base estimators.
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---
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license: mit
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language: en
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tags:
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- tabular-classification
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pipeline_tag: tabular-classification
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library_name: sklearn
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---
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# 🌸 Iris Flower Classification using Ensemble Learning
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This repository focuses on building and evaluating a high-performance machine learning pipeline on the classic **Iris Dataset** using advanced **Ensemble Learning** methodologies. The goal is to optimize multi-class classification accuracy by combining multiple base estimators.
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