Using Shap Values

Introduction to SHAP Values

SHAP (SHapley Additive exPlanations) values are a powerful method for interpreting machine learning models. They provide a unified measure of feature importance, helping us understand how each feature contributes to the final decision made by the model.

In this tutorial, we will explore how to use SHAP values in the context of Keras models to gain insights into model predictions.

Installing Required Libraries

Before we start, ensure that you have the necessary libraries installed. You can do this using pip:

pip install shap tensorflow keras

Building a Sample Keras Model

Let's create a simple Keras model using the popular Iris dataset. We will train the model and then use SHAP values to interpret its predictions.

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Dense
import shap

# Load iris dataset
iris = load_iris()
X = iris.data
y = iris.target

# Split the dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Build the model
model = Sequential()
model.add(Dense(10, activation='relu', input_shape=(X_train.shape[1],)))
model.add(Dense(3, activation='softmax')
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# Train the model
model.fit(X_train, y_train, epochs=50, verbose=0)

Using SHAP to Explain Predictions

Now that we have trained our model, we can use SHAP values to explain its predictions. We will use the KernelExplainer, which is a model-agnostic method suitable for any machine learning model.

# Initialize SHAP explainer
explainer = shap.KernelExplainer(model.predict, X_train)

# Calculate SHAP values for the test set
shap_values = explainer.shap_values(X_test)

Visualizing SHAP Values

SHAP provides several visualization techniques to help us understand the contributions of different features. Here, we will demonstrate the summary plot and the force plot.

# Summary plot
shap.summary_plot(shap_values, X_test, feature_names=iris.feature_names)

The summary plot shows the impact of each feature on the model's output for all instances in the test set. The x-axis indicates the SHAP value, which represents the contribution of a feature to the prediction.

# Force plot for the first instance
shap.initjs()
shap.force_plot(explainer.expected_value, shap_values[0], X_test[0])

The force plot provides a detailed view of how each feature contributes to the prediction for a single instance.

Conclusion

In this tutorial, we have explored the use of SHAP values for interpreting Keras models. We built a simple neural network, calculated SHAP values, and visualized them using summary and force plots. Understanding how features influence model predictions is crucial for building trust in machine learning models, and SHAP is an excellent tool for this purpose.