---
license: apache-2.0
datasets:
- scikit-learn/iris
metrics:
- accuracy
library_name: pytorch
pipeline_tag: tabular-classification
---

# logistic-regression-iris

A logistic regression model trained on the Iris dataset.

It takes two inputs: `'PetalLengthCm'` and `'PetalWidthCm'`. It predicts whether the species is `'Iris-setosa'`.

It is a PyTorch adaptation of the scikit-learn model in Chapter 10 of Aurelien Geron's book 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'.

Code: https://github.com/sambitmukherjee/handson-ml3-pytorch/blob/main/chapter10/logistic_regression_iris.ipynb

Experiment tracking: https://wandb.ai/sadhaklal/logistic-regression-iris

## Metric

The validation set contains 30% of the examples (selected at random using stratification on the target variable):

```
from sklearn.model_selection import train_test_split

X_train, X_val, y_train, y_val = train_test_split(X.values, y.values, test_size=0.3, stratify=y, random_state=42)
```

Accuracy on the validation set: 1.0