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Case Study: Local Explanations for Classification Problem

Case Study: Local Explanations for Classification Problem

Learn how to explain individual model decisions using the LIME framework for a classification case study.

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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.

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main.py
loan_approval.csv
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 ...