Is it necessary to use other data for training and evaluation of the model?
Monday, 13 November 2023 by Hema Gunasekaran
In the field of machine learning, the use of additional data for training and evaluation of models is indeed necessary. While it is possible to train and evaluate models using a single dataset, the inclusion of other data can greatly enhance the performance and generalization capabilities of the model. This is especially true in the
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
Tagged under: Artificial Intelligence, Concept Drift, Data Augmentation, Data Imbalance, Generalization, Overfitting
Is it possible to reuse training sets iteratively and what impact does that have on the performance of the trained model?
Friday, 01 September 2023 by Willem Kok
Iteratively reusing training sets in machine learning is a common practice that can have a significant impact on the performance of the trained model. By repeatedly using the same training data, the model can learn from its mistakes and improve its predictive capabilities. However, it is essential to understand the potential advantages and disadvantages of
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, The 7 steps of machine learning
Tagged under: Artificial Intelligence, Concept Drift, Machine Learning, Model Performance, Overfitting, Training Sets