Machine Learning Classification for Mycological Safety
Supervised learning model for accurate mushroom species classification and safety assessment, implementing advanced feature engineering techniques for interpretable binary classification.
Implemented One-Hot Encoding to transform categorical mushroom attributes (cap shape, color, gill attachment) into numerical vectors for ML processing.
Utilized DecisionTreeClassifier from Scikit-learn for interpretable binary classification (poisonous vs. safe), enabling visual inspection of decision paths.
Rigorous testing using train/test splits and cross-validation to ensure high accuracy and generalization on unseen mushroom specimens.
Unlike black-box models, decision trees provide clear decision paths, making the classification process transparent and debuggable—crucial for safety-critical applications.
Demonstrates ML's potential in public health and safety domains, where accurate classification can prevent poisoning incidents and inform foragers.
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
# Feature engineering with One-Hot Encoding
encoder = OneHotEncoder()
X_encoded = encoder.fit_transform(mushroom_features)
# Train interpretable classifier
clf = DecisionTreeClassifier(max_depth=5)
clf.fit(X_encoded, y_labels)
# Predict safety classification
prediction = clf.predict(new_mushroom_sample)