Ensemble methods Archives - Sinfa Consulting: Data Science & AI Consulting

Leveraging SENet for More Accurate Financial Forecasting

Introduction Financial forecasting is a critical component of any business’s financial planning. The accuracy of these forecasts is essential for the decision-making process in the financial industry. In recent years, there has been a significant increase in the use of machine learning algorithms to make financial predictions. One such technology is SENet, which is being …

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Using Optimal Subsampling and Bootstrap in Machine Learning: Improving Model Accuracy

Introduction Machine learning algorithms have become increasingly popular in recent years as businesses and organizations look for ways to extract value from their data. However, getting accurate predictions from machine learning models is not always easy. One of the biggest challenges is reducing overfitting and bias in the models, which can lead to inaccurate predictions. …

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The Ultimate Solution to Feature Overload: Model-Free Feature Selection for Mass Features

Introduction In today’s digital age, data is everywhere, and with the rise of big data and machine learning, the number of features that can be collected is increasing rapidly. However, while having more data may seem like an advantage, it can often lead to feature overload, which can negatively impact the performance of models. Feature …

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Fraud Detection Using Deep Boosting Decision Trees

Fraud is a major concern for businesses, and it can be challenging to identify fraudulent activities in real-time. Fraudulent activities can cause financial losses, reputation damage, and regulatory fines. The increasing use of digital channels has made it even more difficult to detect fraud. However, advancements in technology have made it possible to identify fraudulent …

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Exploring the Effectiveness of Imbalanced Data Correction Methods in Mixed Linear Regression Models

Introduction In recent years, the amount of data collected in various fields has grown rapidly, and machine learning algorithms have become increasingly popular for analyzing such data. However, a common issue faced when working with large datasets is class imbalance, where one class in the target variable is greatly outnumbered by the other. This imbalance …

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