Search⌘ K
AI Features
Home
Courses
Responsible AI: Principles and Practices

Case Study: Local Explanations for Classification Problem

Learn to build and explain a loan approval classification model using LIME. Understand how to interpret individual model predictions with detailed feature impact visualizations to improve AI transparency and trust.

We'll cover the following...

In this lesson, we’ll build a predictive classification model on the loan approval dataset. We train a machine learning model to predict loan approval decisions. Then, we’ll explain the model prediction on individual examples using the LIME framework.

LIMELIME

Train a classification model to predict loan approval decisions

Let’s use the credit loan dataset to train a machine learning model that predicts the propensity to approve loans.

The problem statement here is to predict the propensity to approve the loan, and we have a binary classification problem at hand.

Python 3.8
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import balanced_accuracy_score, roc_auc_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import precision_score, recall_score
# Create an instance of the random forest classifier
clf = RandomForestClassifier()
# Train the classifier
clf.fit(X_train, y_train)
# Make predictions on the test set
y_pred = clf.predict(X_test)
Y_pred_proba = clf.predict_proba(X_test)[:, 1]
# Model performance metric
# Compute the confusion matrix
cm = confusion_matrix(y_test, y_pred)
#print('\nConfusion Matrix:\n')
print(cm)
# Calculate precision
precision = precision_score(y_test, y_pred)
print("\nPrecision:", precision)
# Calculate recall
recall = recall_score(y_test, y_pred)
print("\Recall:", recall)
# Calculate AUC 
print('ROC AUC:',roc_auc_score(y_test, Y_pred_proba))

The code explanation is given below:

  • Lines 1–5: We import the necessary libraries required for ...