What is model assessment?

What is model assessment?

Model Evaluation is an integral part of the model development process. It helps to find the best model that represents our data and how well the chosen model will work in the future. To avoid overfitting, both methods use a test set (not seen by the model) to evaluate model performance.

What is meant by selecting model?

Model selection is the process of choosing one among many candidate models for a predictive modeling problem. There may be many competing concerns when performing model selection beyond model performance, such as complexity, maintainability, and available resources.

Why is it important to assess our selected model?

Assessment of this performance is extremely important in practice, since it guides the choice of learning method or model, and gives us a measure of the quality of the ultimately chosen model.

How do you choose a classification model?

Here are some important considerations while choosing an algorithm.

1. Size of the training data. It is usually recommended to gather a good amount of data to get reliable predictions.
2. Accuracy and/or Interpretability of the output.
3. Speed or Training time.
4. Linearity.
5. Number of features.

How is model selection used in model assessment?

You would then fit your models to the training data, then use the validation set to perform model selection, and finally, evaluate the very best selected model on the test data to see what generalization performance can be expected of it ( model assessment ).

How is validation set used in model selection?

The training set is used to fit the models; the validation set is used to estimate prediction error for model selection; the test set is used for assessment of the generalization error of the final chosen model. — Page 222, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2017.

How is the training set used in model selection?

The training set is used to train as many models as there are different combinations of model hyperparameters. These models are then evaluated on the validation set, and the model with the best performance on this validation set is selected as the winning model.

Which is the best strategy for model selection?

The recommended strategy for model selection depends on the amount of data available. If plenty of data is available, we may split the data into several parts, each serving a special purpose. For instance, for hyperparameter tuning we may split the data into three sets: train / validation / test.