Explainable AI with Linear Models

Benefits of Linear Models:

• Starting with linear models offers simplicity, aiding in understanding and establishing a baseline for comparison with more complex models.

Causality Caution:

• Avoid making causal conclusions, as relationships may involve proxy variables. Beware of confirmation bias and aim for objective analysis.

Data Exploration:

• Investigate correlation (multicollinearity) and normality of features (distribution of features).
• Detect outliers using Z-score and Interquartile Range (IQR).
• Conduct Exploratory Data Analysis (EDA) including correlation and scatter plots.

Feature Engineering:

• Encode categorical features.
• Consider inverses of features and logical ratios for intuitive feature construction.
• Discretize continuous features to transform them into categorical features.

Finding Non-Linear Relationships:

• Utilize domain knowledge, data exploration (e.g., correlations), and non-linear models (e.g., Random Forest, XGBoost, DNN) to identify patterns.

Feature Selection:

• Use correlation-based selection and Variance Inflation Factor (VIF) to identify and remove redundant features.
• Eliminate irrelevant features lacking predictive power.
• Evaluate predictive power through feature groups and variable clustering methods.

Considerations in Feature Selection:

• Group features based on similarity.
• Use variable clustering methods like k-means or hierarchical clustering.
• Ensure data authenticity and availability in production.
• Validate feature predictiveness across time.
• Prioritize interpretable features and ensure legal and ethical compliance.

Hierarchical Clustering for Feature Selection:

• Employ hierarchical clustering to group features based on correlations while retaining interpretability.

Choosing Model:

• Consider linear and polynomial models.

Training Model:

• Choose a cost function (e.g., LASSO for MSE with regularization).
• Adjust learning rate and number of epochs.
• Specify polynomial degree for polynomial models (e.g., 2 or 3).

Model Evaluation:

• Assess model performance using metrics like MSE, R-squared, and RMSE.
• Validate model robustness through cross-validation.
• Analyze learning curves and residuals to understand model behavior and shortcomings.
• Compare linear model results with other models to ensure optimal choice.

Visualizing Linear Regression Performance:

• Utilize scatter plots, correlation heatmaps, weight plots, mean effect plots, individual effect plots, trend effect plots, partial dependence plots, and SHAP values to interpret model performance.

Mitigating Confirmation Bias:

• Employ multiple Explainable AI (XAI) methods to reduce confirmation bias, providing transparency and aiding in understanding model decisions.