README.md

metadata

library_name: sklearn
tags:
  - sklearn
  - scikit-learn
  - classification
  - wine
  - random-forest

Wine Classification Model

A RandomForestClassifier model trained on the UCI Wine dataset for wine classification.

Model Details

• Model Type: RandomForestClassifier
• Dataset: UCI Wine Dataset
• Number of Features: 13
• Number of Classes: 3
• Classes: class_0, class_1, class_2

Model Parameters

• n_estimators: 100
• max_depth: 6
• random_state: 42

Usage

Using Hugging Face Hub

from huggingface_hub import hf_hub_download
import joblib

# Download and load the model
model_path = hf_hub_download(repo_id="alirisheh/test1", filename="model.joblib")
model = joblib.load(model_path)

# Make predictions
predictions = model.predict(X_test)

Using the Hugging Face Inference API

You can also use this model with the Hugging Face Inference API once it's deployed.

Training

The model was trained on the scikit-learn wine dataset with an 80/20 train/test split.

Evaluation

The model achieves high accuracy on the test set. See model_metadata.json for detailed metrics.

Files

• model.joblib: The trained scikit-learn model
• model_metadata.json: Model metadata and training information
• sample_input.json: Sample input for testing