Is the usually recommended data split between training and evaluation close to 80% to 20% correspondingly?
The usual split between training and evaluation in machine learning models is not fixed and can vary depending on various factors. However, it is generally recommended to allocate a significant portion of the data for training, typically around 70-80%, and reserve the remaining portion for evaluation, which would be around 20-30%. This split ensures that
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Further steps in Machine Learning, Big data for training models in the cloud
Is it correct that if dataset is large one needs less of evaluation, which means that the fraction of the dataset used for evaluation can be decreased with increased size of the dataset?
In the field of machine learning, the size of the dataset plays a important role in the evaluation process. The relationship between dataset size and evaluation requirements is complex and depends on various factors. However, it is generally true that as the dataset size increases, the fraction of the dataset used for evaluation can be
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, Deep neural networks and estimators
What is a test data set?
A test data set, in the context of machine learning, is a subset of data that is used to evaluate the performance of a trained machine learning model. It is distinct from the training data set, which is used to train the model. The purpose of the test data set is to assess how well
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
What are the three steps in which each machine learning algorithm will be covered?
In the field of Artificial Intelligence, particularly in the domain of Machine Learning with Python, there are three fundamental steps that are typically followed in covering each machine learning algorithm. These steps are essential for understanding and implementing machine learning algorithms effectively. They provide a structured approach to building and evaluating models, enabling practitioners to
- Published in Artificial Intelligence, EITC/AI/MLP Machine Learning with Python, Introduction, Introduction to practical machine learning with Python, Examination review
How does the test split parameter determine the proportion of data used for testing in the dataset preparation process?
The test split parameter plays a important role in determining the proportion of data used for testing in the dataset preparation process. In the context of machine learning, it is essential to evaluate the performance of a model on unseen data to ensure its generalization capabilities. By specifying the test split parameter, we can control
What can you do if you identify mislabeled images or other issues with your model's performance?
When working with machine learning models, it is not uncommon to encounter mislabeled images or other issues with the model's performance. These issues can arise due to various reasons such as human error in labeling the data, biases in the training data, or limitations of the model itself. However, it is important to address these