Interpretability is an essential aspect of machine learning models when developing predictive models or deploying machine learning solutions in organizations. Model interpretability refers to the ability to explain the rationale behind a model’s decision. The interpretable models can help us understand how the model arrives at its predictions, which is crucial for building trust in the model’s results, making informed decisions based on the model’s outcomes, and detecting and fixing potential issues, including bias, in the model’s predictions.
Model interpretability is difficulty to achieve in complex machine learning models such as deep neural networks that consist of millions of parameters, and whose decision-making is highly nonlinear. However, there are some popular techniques for interpreting complex models, including:
1. **SHAP (SHapley Additive exPlanations)**: SHAP is a model-agnostic tool that assigns each feature’s contribution to the model’s prediction. SHAP assigns a Shapley value to each feature, indicating how each input feature contributes to the model’s output. In essence, SHAP combines game theory with Bayesian reasoning to compute each feature’s expected contribution to a given prediction.
2. **LIME (Local Interpretable Model-Agnostic Explanations)**: LIME is another model-agnostic tool that generates local explanations for the model’s decision. LIME works by approximating the learned model around the instance of interest with a simple interpretable model, such as a linear or decision tree model, that is easier to interpret, and hence the model’s decision is explainable. LIME helps uncover the decision’s rationale at the local level by explaining how the model arrives at its prediction for a single observation.
3. **Decision tree**: Decision tree models are highly interpretable because their decision-making process can be easily visualized and understood. Decision trees split data based on a feature that divides the data into the purest possible subsets. These splits are repeated recursively until the data is entirely homogeneous, or until some stopping criterion is met.
4. **Linear regression**: Linear regression models are simple to interpret because they consist only of a few fixed parameters that directly determine each input feature’s impact on the model’s prediction. The coefficients from a trained linear regression model are excellent indicators of the input feature’s relative importance.
In conclusion, model interpretability is crucial for building trust and transparency in machine learning models. There exist several techniques for interpreting complex machine learning models, including SHAP, LIME, decision trees, and linear regression. These techniques can help us understand how a model arrives at its prediction and explain its decision-making process to stakeholders.