The definition and application of interpretive algorithms in machine learning

An important issue in machine learning is understanding the reasons for model predictions. Although we can know the function of the algorithm through existing algorithm models, it is difficult to explain why the model produces such prediction results. However, interpretive algorithms can help us identify outcomes of interest and meaningful variable effects.

Explanatory algorithms allow us to understand the relationships between variables in a model, rather than just predicting outcomes. Therefore, by using multiple algorithms, we can better understand the relationship between the independent and dependent variables in a given model.

Explanatory Algorithm

Linear/logistic regression is a statistical method used to model a dependent variable with one or more independent variables linear relationship between them. This method can help us understand the relationship between variables by testing the sum of coefficients.

Decision tree is a machine learning algorithm that makes decisions by creating a tree-like model. It helps us understand the relationship between variables by analyzing the rules for splitting branches.

Principal Component Analysis (PCA): A dimensionality reduction technique that projects data into a low-dimensional space while retaining as much variance as possible. PCA can be used to simplify data or determine feature importance.

LIME (Local Interpretable Model-Agnostic Explanations): Explain the predictions of any machine learning model by approximating the model around the predictions by building simpler models using techniques such as linear regression or decision trees .

SHAPLEY (Shapley Additive explanations): Explain the predictions of any machine learning model by calculating the contribution of each feature to the prediction using a method based on the concept of "marginal contribution". In some cases it is more accurate than SHAP.

SHAP (Shapley Approximation): A prediction method that explains any machine learning model by estimating the importance of each feature in prediction. SHAP uses a method called a "joint game" to approximate Shapley values ​​and is generally faster than SHAPLEY.

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