license: mit
metrics:
- accuracy
- precision
- recall
- f1
library_name: sklearn
pipeline_tag: tabular-classification
tags:
- biology
- 🐧
Palmer Penguins Species Classifier
Model description
This model is a scikit-learn classifier trained to predict the species of penguins in the Palmer Penguins dataset. The dataset contains measurements of penguin species including the species itself, making it suitable for classification tasks.
The model uses features such as culmen length, culmen depth, flipper length, and body mass to predict the species of penguins.
Intended uses & limitations
The model is intended for classifying the species of penguins based on their physical measurements. It can be used in applications related to penguin species classification and analysis.
Limitations:
- The model's performance may vary depending on the quality and representativeness of the input data.
- It is trained specifically on the Palmer Penguins dataset and may not generalize well to other penguin datasets or species outside of the dataset.
Training data
The model is trained on the Palmer Penguins dataset, which contains measurements of penguin species including Adelie, Chinstrap, and Gentoo. The dataset is publicly available and can be accessed here.
Training procedure
The model is trained using scikit-learn, a popular machine learning library in Python. It uses a classification algorithm (e.g., Random Forest, Support Vector Machine) to learn the relationship between the input features (culmen length, culmen depth, flipper length, body mass) and the target variable (species).
Model in action
# Load the model
from sklearn.externals import joblib
model = joblib.load('path/to/your/model.pkl')
# Input features
features = {
'culmen_length_mm': 39.1,
'culmen_depth_mm': 18.7,
'flipper_length_mm': 181,
'body_mass_g': 3750
}
# Predict species
predicted_species = model.predict([list(features.values())])[0]
print(f"Predicted species: {predicted_species}")
🐧 Disclaimer
No penguins were harmed while training this model 🐧.
We were noot involved in collecting the 🐧 data.