Model: ML confusion matrix for Isolation

Purpose:

Simulated dataset for exploring the performance of a machine learning model in classifying individuals as "at risk" or "not at risk" based on a set of features.
Used for testing model accuracy, evaluating feature importance, and understanding model behavior under varying conditions.
Key Components:

Features:
Synthetic, not based on real-world data.
5 features:
Question1, Question2, Question3 (random integers between 1 and 6)
Trait1, Trait2 (randomly generated numbers)
Represent potential questionnaire responses or other relevant attributes.
Target Variable:
Binary (0 for "not at risk", 1 for "at risk")
Simulated with a 70/30 class distribution (30% "not at risk", 70% "at risk")
Sample Size:
2000 samples
Machine Learning Model:
Random Forest Classifier with 100 estimators and a maximum depth of 10
Trained and evaluated using standard metrics (accuracy, classification report, confusion matrix)
Considerations:

Simulated Data: Does not reflect the complexity and nuances of real-world data.
Feature Meaning: Actual meanings of features are not specified, limiting interpretation of results.
Class Balance: Adjusted to be more balanced, but still not representative of all real-world scenarios.
Next Steps:

Validate with Real-World Data: Assess model performance on actual data to ensure generalisability.
Incorporate Additional Features: Explore incorporating more complex and realistic features.
Explore Different Models: Experiment with other algorithms to compare performance.
Address Class Imbalance: Consider techniques like oversampling or under-sampling to handle imbalanced datasets effectively.