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README.md

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+---
+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