CandleFocus Glossary Editor
Overfitting is a common problem which occurs when a data model is created from a limited set of data and is too closely aligned with specific data points. As a result, the model produces results which are flawed and unreliable for investment decisions. Overfitting is usually the result of trying to avoid the issue of underfitting which occurs when the data model is too simple and has too few data points.
Overfitting occurs when the model has memorized the data set rather than actually learning from it. The model is then likely to fail to generalize the data, unable to make accurate predictions, or unable to accurately determine variables that play a role in the decision process. The model becomes overly complex when overfitting occurs and this complexity can hurt the performance of the model and impede its ability to produce valuable and accurate results.
The best way to avoid overfitting is to use larger data sets and more complexity in the model. The model should also include validation tests and discuss lessons learned for future data modeling. Cross-validation helps to ensure that the model does not overfit by repeatedly running the model on different combinations of data and making sure that the results are consistent. Data scientists can use regularization techniques such as lasso and ridge regression to reduce the complexity of the model and thus mitigate the effect of overfitting.
By using larger data sets with more complexity and validation tests, financial professionals can avoid overfitting and reap the rewards of accurate data models that can be used to make informed decisions on investments. The right combination of data and model complexity will allow financial professionals to meet their investment goals in a more effective manner.
Overfitting occurs when the model has memorized the data set rather than actually learning from it. The model is then likely to fail to generalize the data, unable to make accurate predictions, or unable to accurately determine variables that play a role in the decision process. The model becomes overly complex when overfitting occurs and this complexity can hurt the performance of the model and impede its ability to produce valuable and accurate results.
The best way to avoid overfitting is to use larger data sets and more complexity in the model. The model should also include validation tests and discuss lessons learned for future data modeling. Cross-validation helps to ensure that the model does not overfit by repeatedly running the model on different combinations of data and making sure that the results are consistent. Data scientists can use regularization techniques such as lasso and ridge regression to reduce the complexity of the model and thus mitigate the effect of overfitting.
By using larger data sets with more complexity and validation tests, financial professionals can avoid overfitting and reap the rewards of accurate data models that can be used to make informed decisions on investments. The right combination of data and model complexity will allow financial professionals to meet their investment goals in a more effective manner.