First things first, let’s define what we mean by “attribution” in the context of machine learning. In simple terms, it’s a way to understand which features are most important for making predictions. For example, if you have a model that predicts whether or not someone will buy a product based on their age and income level, an attribution might show that age is more important than income in determining the outcome.
Now, why we care about attributions for non predicted classes specifically. In many cases, our models are trained to predict whether or not something belongs to a certain category (e.g., spam vs. ham email). But what if our model makes an error and classifies an email as spam when it’s actually legitimate? How can we understand why the model made that mistake?
That’s where attributions for non predicted classes come in handy! By analyzing which features contributed to the model’s decision, we can identify areas where our data or model might be flawed. For example, if a particular feature (like the sender’s email address) is consistently associated with false positives, it might indicate that there are some legitimate emails coming from that domain that should not be classified as spam.
So how do we actually calculate attributions for non predicted classes? There are several methods available, but one popular approach involves using a technique called Shapley values (which is named after the economist who invented it). Essentially, this method calculates the contribution of each feature to the model’s output by measuring how much its value changes when that feature is removed from the dataset.
Here’s an example code snippet using Scikit-Learn and Shapely library for attribution calculation:
# Import necessary libraries
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix
import shap
from shap.plots import barplot
# Load the dataset (in this case, a simple binary classification problem)
# Use pd.read_csv() to read the data from csv files and store them in variables X and y
X = pd.read_csv('data.csv')['features']
y = pd.read_csv('labels.csv')['target']
# Split into training and testing sets
# Use train_test_split() to split the data into training and testing sets
# Set test_size to 0.2 to allocate 20% of the data for testing
train_index, test_index = train_test_split(np.arange(len(X)), test_size=0.2)
# Use the train_index and test_index to split the data into training and testing sets
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
# Train the model (in this case, a simple logistic regression)
# Use LogisticRegression() to create a logistic regression model
model = LogisticRegression()
# Use fit() to train the model on the training data
model.fit(X_train, y_train)
# Evaluate the model on the test set
# Use predict() to make predictions on the test data
y_pred = model.predict(X_test)
# Use accuracy_score() to calculate the accuracy of the predictions
accuracy = accuracy_score(y_test, y_pred)
# Use confusion_matrix() to calculate the confusion matrix of the predictions
confusion = confusion_matrix(y_test, y_pred)
# Print the accuracy and confusion matrix
print("Accuracy: {:.2f}%".format(100*accuracy))
print("Confusion Matrix:\n", confusion)
# Calculate the attributions for non predicted classes (in this case, false positives)
# Use KernelExplainer() to create an explainer object
# Pass in the predict_proba function of the model and the test data
explainer = shap.KernelExplainer(model.predict_proba, X_test)
# Use shap_values() to calculate the Shapley values for the false positive predictions
shap_values = explainer.shap_values(X_test[y_pred != y_test])
# Print the Shapley values for each feature
print("Shapley Values for False Positives:")
for feature in range(len(X.columns)):
# Use np.mean() to calculate the mean Shapley value for each feature
print(f"{X.columns[feature]}: {np.mean(shap_values[:, feature]):.4f}")
And that’s it! By using attributions for non predicted classes, we can gain valuable insights into our models and data, which can help us improve their performance over time. So next time you encounter a false positive or other unexpected result, don’t panic just take a closer look at the features that contributed to the decision and see if there are any patterns or trends that might be worth exploring further!