metadata

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