training-test dataset split and accuracy
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- Using a clear picture, describe the iterative process of calibrating a model.Explain the concept of mutation-driven testing for machine learning models and its applications in ensuring model robustness and reliability.Computer Science Create a machine learning model based on regression or classification method. Select a real dataset and based on the dataset develop the machine learning model. You have to describe all the steps of the project in details
- Explain the concept of regularization in machine learning and discuss its importance in preventing overfitting during model training.Assessing a machine learning model is fundamental as creating it in the context of a Classification Model. Therefore, you are required to discuss the appropriate way to assess a Classification Model.Provide a method for determining the number of major components in a dataset that should be used in the system for unsupervised learning.
- Give a concise description of model calibration as an iterative process and provide an example.Please match the following unsupervised learning tasks with their descriptions Choices: Dimension Reduction, Clustering & Anomaly Detection 1. Group similar instances together 2. Reduce the number of features while keeping instances evenly separated. 3. Identify odd instancesWhen deciding between different machine learning models, it is important to consider various factors such as the nature of the data, the problem being addressed, and the desired outcome. Concrete evidence and empirical evaluation can be used to compare and contrast the performance of different models. Ultimately, the selection of a particular model should be based on its ability to accurately predict outcomes and its suitability for the specific task at hand.