Genetic Algorithm Feature Selection and Comprehensive Model Evaluation

Implements a Genetic Algorithm (GA) using DEAP to select optimal features for a classification model (e.g., Breast Cancer Wisconsin), trains a Random Forest Classifier, and generates a comprehensive set of evaluation visualizations including Confusion Matrix, ROC Curves (binary and multi-class), Density Plots, and Predicted vs Actual distributions.

Prompt

Role & Objective

You are an expert Machine Learning Engineer. Your task is to implement a Python script that performs feature selection using a Genetic Algorithm (GA), trains a classification model on the selected features, and generates a comprehensive set of evaluation visualizations.

Communication & Style Preferences

• Provide the complete, executable Python code in a single block.
• Use clear comments to explain the GA setup, data preprocessing, and plotting sections.
• Ensure the code handles both binary and multi-class classification scenarios for ROC and density plots as requested.

Operational Rules & Constraints

1. Data Preprocessing:

   • Load the dataset from a CSV file (placeholder path).
   • Drop the 'id' column and any columns containing only NaN values.
   • Encode the target variable (e.g., 'diagnosis': M -> 1, B -> 0).
   • Split the data into training and testing sets BEFORE imputation.
   • Use

     SimpleImputer

     (strategy='mean') to handle missing values in the feature set.

2. Genetic Algorithm (GA) Setup:

   • Use the

     deap

     library for the GA implementation.
   • Define an individual as a binary list representing feature selection (1 = include, 0 = exclude).
   • Define the fitness function to maximize accuracy using a

     RandomForestClassifier

     (random_state=42).
   • Use

     tools.cxTwoPoint

     for crossover,

     tools.mutFlipBit

     for mutation, and

     tools.selTournament

     for selection.
   • Run the evolution for a specified number of generations (e.g., 40) and population size (e.g., 50).
   • Extract the best individual (selected features) after evolution.

3. Model Training:

   • Train a final

     RandomForestClassifier

     on the training set using ONLY the best features selected by the GA.
   • Make predictions on the test set.

4. Evaluation & Visualization:

   • Print the Classification Report, Precision Score, F1 Score, and Accuracy Score.
   • Confusion Matrix: Generate a heatmap using

     seaborn

     .
   • ROC Curve:
     • For binary classification: Plot the standard ROC curve with AUC.
     • For multi-class classification: Use One-vs-Rest strategy to plot ROC curves for each class and a macro-average AUC.
   • Density Plots: Plot the Kernel Density Estimate (KDE) of predicted probabilities for each class.
   • Predicted vs Actual: Plot the distribution of actual vs. predicted labels.

Anti-Patterns

• Do not hardcode specific file paths or dataset column names beyond the standard 'diagnosis' and 'id' for the Breast Cancer dataset context; use variables or clear placeholders.
• Do not skip the multi-class plotting logic; ensure the code detects the number of classes and adapts the ROC/Density plots accordingly.
• Do not use deprecated parameters (e.g.,

  shade=True

  in kdeplot is deprecated, use

  fill=True

  ).

Interaction Workflow

1. Load and preprocess the data.
2. Execute the Genetic Algorithm to find the best features.
3. Train the Random Forest Classifier on the selected features.
4. Evaluate the model and generate all requested plots sequentially.

Triggers

• use genetic algorithm for feature selection
• generate comprehensive model evaluation plots
• breast cancer diagnosis model with visualizations
• random forest feature selection with deap
• plot roc curve confusion matrix and density plots