iliih / titanic_machine_learning Goto Github PK

The task "TML-1 Neural Network Accuracy Optimization" focuses on enhancing the performance and accuracy of a neural network model denoted as TML-1. This task encompasses a series of steps aimed at improving the model's predictive capabilities and reducing error rates.

Key Objectives:

Performance Enhancement: The primary goal is to boost the overall performance of the TML-1 neural network model. This involves optimizing various components such as architecture, hyperparameters, and optimization algorithms to achieve better accuracy and efficiency.

Accuracy Improvement: Another crucial objective is to enhance the accuracy of the TML-1 model. This entails refining the model's training process, fine-tuning parameters, and possibly exploring advanced techniques such as ensemble methods or transfer learning to achieve superior predictive accuracy.

Error Reduction: Minimizing errors and inaccuracies in the model's predictions is essential for its reliability and effectiveness. Strategies for error reduction may include data preprocessing, feature engineering, and regularization techniques to mitigate overfitting or underfitting.