mushroom poison

? Mushroom Poison Detection Model (Overview) A Mushroom Poison Detection Model is a machine learning classification model used to predict whether a mushroom is edible or poisonous based on its physical characteristics. Key Components: Dataset Usually uses the UCI Mushroom Dataset. Contains attributes like: Cap shape, cap color Odor Gill size and spacing Stalk shape, color, etc. Whether it's poisonous (p) or edible (e) Data Preprocessing All features are categorical, so we apply encoding like: Label Encoding or One-Hot Encoding Model Training Common classifiers used: Decision Tree Random Forest Naive Bayes Logistic Regression Evaluation Metrics: Accuracy, Precision, Recall, Confusion Matrix Goal: To help identify toxic mushrooms and prevent accidental poisoning, especially for mushroom foragers or in automated identification apps.