Predictive Model Validation

You must divide your dataset into two sets: training and test datasets, in order to test the predictive analytic model you constructed. These datasets should be chosen at random and should accurately reflect the real population.

  • Both the training and test datasets should include similar data.
  • In most cases, the training dataset is much bigger than the test dataset.
  • Overfitting mistakes can be avoided by using the test dataset.
  • To assess how well the trained model will function, it is tested against test data.

Some data scientists like to have a third dataset, called a validation dataset, that has comparable properties to the previous two. The concept is that if you’re actively utilizing your test data to develop your model, you should validate the model’s correctness using a distinct (third) set.

Having a validation dataset that wasn’t utilized during the model’s construction ensures an unbiased assessment of the model’s correctness and efficacy.

Keep in mind that if you’ve created numerous models with different methods, the validation sample might also assist you in determining which model performs the best.

  • Double-check your work when designing and testing the model. Be especially suspicious if the model’s performance or accuracy appears to be too good to be true. Errors in prediction machine learning may occur in the most unexpected places. Erroneous findings might occur from incorrectly computing dates for time series data, for example.

How to validate predictive models? Following the creation of the model with the training dataset, model validation metrics must be computed to determine whether the model generated good projected values for the variable under investigation. For each sample of the training and validation datasets, the values of this variable are known. Intuitively, one wishes to determine whether the values predicted by the model previously defined are far from the values of the ml model validation datasets for each sample in the validation dataset.

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Cross-validation is a widely used predictive model validation technique. The same approach of separating testing and training datasets applies here: The model is built using the training data, and it is then tested against the testing set to forecast data it hasn’t seen before, which is one approach to assess its accuracy.

The historical data is divided into X number of subgroups in cross-validation. The rest of the subsets are utilized as training data each time a subset is chosen to be used as test data. The previous test set then becomes one of the training sets on the following run, and one of the former training sets becomes the test set.

The approach is repeated until each of the X numbers of sets has been utilized as a test set.

You may utilize every data point in your historical data for both training and testing with cross-validation. This method is more successful than just dividing your historical data into two sets, utilizing the one with more data for training and the other for testing, and calling it a day.

When you cross-validate your data, you’re guarding against choosing test data that’s too simple to predict at random, giving the mistaken impression that your model is correct.

Alternatively, if you choose test data that is too difficult to forecast, you may come to the incorrect conclusion that your model isn’t working as well as you had planned.

  • Cross-validation is extensively used to evaluate the performance of several models as well as to check their correctness.

Bias and variance

Bias and variance are two kinds of mistakes that might occur while developing an analytical model.

Bias is the outcome of creating a model that greatly simplifies the presentation of relationships between data points in the historical data that was used to create the model.

The outcome of creating a model that is expressly particular to the data used to create the model is variance.

Better validation of predictive models can be achieved by striking a compromise between bias and variation – lowering variance while accepting some bias. As a result of this trade-off, fewer sophisticated prediction models are frequently built.


Here are some suggestions to explore that may assist you in getting back on track:

  • Experiment with different variables and derived variables. Always keep an eye out for factors with predictive power.
  • Consult with business domain experts on a regular basis to assist you to make sense of the data, choosing variables, and evaluating the model’s output.
  • Double-check your work at all times. You could have missed something that you thought was proper but wasn’t. Such faults might appear in your dataset’s values of a predictive variable, or in the preprocessing you performed on the data.
  • Try a different algorithm if the one you used isn’t producing any results. For example, you may attempt a few different classification algorithms, and one of them may perform better than the others based on your data and the business objectives of your model.