Challenge: Solving Task Using XGBoost

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The "Credit Scoring" dataset is commonly used for credit risk analysis and binary classification tasks. It contains information about customers and their credit applications, with the goal of predicting whether a customer's credit application will result in a good or bad credit outcome.

Your task is to solve classification task on "Credit Scoring" dataset:

Create Dmatrix objects using training and test data. Specify enable_categorical argument to use categorical features.
Train the XGBoost model using the training DMatrix object.
Set the split threshold to 0.5 for correct class detection.

Note

'objective': 'binary:logistic' parameter means that we will use logistic loss (also known as binary cross-entropy loss) as an objective function when training the XGBoost model.

Løsning

import numpy as np

import xgboost as xgb

from sklearn.datasets import fetch_openml

from sklearn.model_selection import train_test_split

from sklearn.metrics import f1_score

# Load the Credit Scoring dataset

data = fetch_openml(name="credit-g", version=1, parser='auto')

X = data.data

y = data.target

# Convert target to binary (1: Good, 0: Bad)

y = (y == 'good').astype(int)

# Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create DMatrix objects for XGBoost with categorical features enabled

dtrain = xgb.DMatrix(X_train, label=y_train, enable_categorical=True)

dtest = xgb.DMatrix(X_test, label=y_test, enable_categorical=True)

# Set hyperparameters

params = {

'objective': 'binary:logistic',

}

# Train the XGBoost classifier

model = xgb.train(params, dtrain)

# Make predictions

y_pred = model.predict(dtest)

y_pred_binary = (y_pred > 0.5).astype(int)

# Calculate F1-score

f1 = f1_score(y_test, y_pred_binary)

print(f'F1-score: {f1:.4f}')

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Sektion 3. Kapitel 6

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