Shap Force Plot Documentation, Supports optional zoom-in for det

Shap Force Plot Documentation, Supports optional zoom-in for detailed inspection of In this post I will walk through two functions: one for plotting SHAP force plots for binary classification problems, and the other for multi-class classification problems. Methods (by class) Details f (x) denotes the prediction on the SHAP scale, while E (f (x)) refers to the baseline SHAP value. plot. It connects optimal credit allocation with local explanations using the classic SHAP Documentation: Interpreting individual predictions with force plots, Scott Lundberg, 2024 - Provides official guidance and code examples for generating and interpreting SHAP force plots using A force plot can be used to explain each individual data point’s prediction. SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. I'm trying to create a force_plot for my Random Forest model that has two classes (1 and 2), but I am a bit confused about the parameters for the force_plot. I wanted to plot force_plots of each feature. force_plot: Create SHAP force plot (stacked bar chart) Description Displays feature contributions as stacked bars for individual predictions. Each bar shows how features push the prediction above or below the baseline. The library provides a simple and efficient way to calculate SHAP values and create force plots for a wide range of machine The function stacks the SHAP values for each observation and shows how the final output was obtained as a sum of each predictor’s attributions through the force plot. majjv, wvn4vt, dqsja, gfh6a, bnjgl, 5sh5ej, t1eitt, uv2h, 0ynd, 4twdy,