scikit-learn classification iris-dataset license:apache-2.0 region:us
---
license: apache-2.0
tags:
- classification
- scikit-learn
- iris-dataset
library_name: scikit-learn
---
## Scikit-learn with Iris Dataset

This repository uses Scikit-learn for classification on the Iris dataset. The model used is a Logistic Regression model.

The Iris dataset is a multivariate data set introduced by the British statistician and biologist Ronald Fisher in his 1936 paper. The data set consists of 50 samples from each of three species of Iris flowers (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the lengths and the widths of the sepals and petals.

The training and evaluation process is done using Scikit-learn, a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms.

# Classification Report

```
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        12
           1       1.00      1.00      1.00        10
           2       1.00      1.00      1.00         8

    accuracy                           1.00        30
   macro avg       1.00      1.00      1.00        30
weighted avg       1.00      1.00      1.00        30
```

# Usage

This model has the following instructions for use.

```
# pip install scikit-learn
# pip install joblib
# pip install huggingface_hub

import joblib
from huggingface_hub import snapshot_download


model_path = snapshot_download("SoAp9035/iris-perfect")

model = joblib.load(model_path + "/iris_perfect.pkl")

print(model.predict([[5.9,3.3,3.9,1.1]]))
# [1]
```

0 - Iris Setosa |
1 - Iris Versicolour |
2 - Iris Virginica

# Hyperparameters

```
{'C': 1.0,
'class_weight': None,
'dual': False,
'fit_intercept': True,
'intercept_scaling': 1,
'l1_ratio': None,
'max_iter': 100,
'multi_class': 'auto',
'n_jobs': None,
'penalty': 'l2',
'random_state': None,
'solver': 'lbfgs',
'tol': 0.0001,
'verbose': 0,
'warm_start': False}
```