Introduction
From numerous machine learning models and algorithms available to solve different types of problems, it is often difficult for data scientists to decide which model to use for a specific problem. This decision can be significantly complicated by the fact that, for a given problem, more than one model can be used to create a solution. In such situations, data scientists may use a model selection methodology that informs them how to choose the best model, which by definition requires certain criteria which can deliberately assess the quality, accuracy and reliability of the models in terms of the problem.
Model Selection
Model selection is a process of evaluating and comparing one or more models through some performance measure to choose the best model with respect to the problem and data given. It helps to bridge the gap between designed models giving the hypothesis of structures and the optimal model which is obtained by the model selection process and achieves the best results for the specific set of input data.
Model selection criteria
In practice, data scientists need to have a set of appropriate criteria for model selection. Without a set of agreed upon criteria, model selection can be a cumbersome and heuristic process. Some of the different criteria that can be used to select an appropriate model include, accuracy, precision, speed, scalability and resource cost.
Accuracy
The first criteria in model selection process is accuracy, which measures how accurately a model is able to classify, predict or estimate the data. Accuracy is the most important criteria, especially when the user is looking for precise output.
Precision
Precision measures the accuracy of a models predictions when there is a degree of uncertainty. For example, if the model is predicting a person or objects characteristics or behavior and a higher precision is desired, then it is important to select a model that can predict these characteristics or behaviors with a high degree of certainty.
Speed
Speed is an important criterion in terms of the performance of the model and the time it takes for it to make predictions. As the speed of prediction is directly related to the scalability of a model, it is important to select a model which is less redundant and can make predictions in a shorter amount of time.
Scalability
The scalability of a model is a key determinant of the models performance. Scalability refers to the capability of a model to quickly adapt to changes in data. With increasing data, the accuracy of a model can decrease due to the models inability to rapidly adapt to changes in data. Therefore, it is important to select a model with a good capacity of scalability, which is capable of quickly adjusting to changes in data.
Resource cost
Resource cost is an important factor to consider when selecting a model. When selecting a model, it is important to consider the resource cost required to deploy and maintain the model. If the cost is too high, then it may not be feasible to use the model, since it would be more expensive than the benefit the model can deliver.
Conclusion
In conclusion, model selection is an important process in the development of a successful machine learning model. It helps data scientists to select the most appropriate model for a given problem and data set by using a set of agreed upon criteria which assess the quality, accuracy and reliability of the models in terms of the problem. The various criteria used in model selection include accuracy, precision, speed, scalability and resource cost. These criteria can be used to assess and evaluate the models in order to choose the best model with respect to the problem and data given.
