Considering the rapidly expanding area of textual data, extracting meaningful insights is a significant challenge. With our novel method for automatic keyphrase extraction, which integrates natural ...

Generative AI - Intern (Personalisation & CI) ING's goal is to enable people to "make the difference" and empower them to stay a step ahead in life and in business. We are one of the largest banks in ...

This score, along with structured features, was used to train a Random Forest classifier. Model performance was evaluated using accuracy, precision, recall and F1 score. A 10-fold stratified ...

Supervised classification uses random forest (RF), support vector machine (SVM), classification and regression trees (CARTs), gradient boosting trees (GBTs), and naïve Bayes. An accuracy assessment ...

The classification models built on class imbalanced data sets tend to prioritize the accuracy of the majority class, and thus, the minority class generally has a higher misclassification rate.

In the second part of the analysis, three machine learning models—Logistic Regression, Random Forest, and XGBoost—were implemented for predictive performance. Logistic Regression outperformed others ...

Machine Learning project to detect fraudulent credit card transactions using the Random Forest algorithm. Includes data preprocessing, handling class imbalance with SMOTE, and performance evaluatio ...

Then, three different supervised machine learning algorithms, including logistic regression, support vector classification, and random forest, were attempted to construct the diagnostic prediction ...