Key Components of Model Validation
1. Data Validation
- Quality: Dropping missing values, detecting outliers, and errors in the data. This prevents the model from learning from incorrect data or misinformation.
- Relevance: Ensuring that the data is a true representation of the underlying problem that the model is designed to solve. Use of irrelevant information may end up leading to wrong conclusions.
- Bias: Ensuring that the data has appropriate representation for the model to avoid reproducing biased or inaccurate results. Using methods such as analyzing data demographics and employing unbiased sampling can help.
2. Conceptual Review
- Logic: Criticizing the logic of the model and examining whether it is useful for the problem under consideration. This includes finding out if the selected algorithms and techniques are suitable.
- Assumptions: Understanding and critically evaluating the assumptions embedded in model building. Expectations that are not based on assumptions can result in inaccurate forecasts.
- Variables: Relevance and informativeness of the selected variables about the purpose of the model. Extraneous variables can also lead to poor model predictions.
3. Testing
- Train/Test Split: Splitting the data into two – the training set to develop the model and the testing set to assess the model’s prediction accuracy on new observations. This helps determine the capability of the model to make correct predictions with new data.
- Cross-validation: The basic principle of cross-validation is that the data is divided into a user defined number of folds and each fold is considered as validation set while training on the remaining ones. This gives a better insight to model’s performance than the train/test split approach.
What is Model Validation and Why is it Important?
Have you ever wondered if there is a way to check or evaluate the performance of a machine learning model you’ve trained? Is there a way or method to understand how the model responds to new or unseen data? The answer is yes, and it’s called Model Validation.
Before diving deeper into the article, let’s take a look at the article’s outline:
Table of Content
- What is Model Validation?
- Types of Model Validation
- 1. In-Sample Validation
- 2. Out-of-Sample Validation
- Importance of Model Validation
- Key Components of Model Validation
- 1. Data Validation
- 2. Conceptual Review
- 3. Testing
- Achieving Model Generalization
- Model Validation Techniques
- Benefits of Model Validation
- Conclusion
- Model Validation -FAQs