Common Machine Learning Obstacles

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  By Seth DeLand, Product Marketing Manager, Data Analytics, MathWorksEngineers and scientists who are modeling with machine learning often face challenges when working with data.

Two of the most common obstacles relate to choosing the right classification model and eliminating data overfitting.

Classification models assign items to a discrete group or class based on a specific set of features.

Determining the best classification model often presents difficulties given the uniqueness of each dataset and desired outcome.

Overfitting occurs when the model is too closely aligned with limited training data that may contain noise or errors.

An overfit model is not able to generalize well to data outside the training set, limiting its usefulness in a production system.

 By integrating scalable software tools and machine learning techniques, engineers and scientists can identify the best model and protect against overfitting.

Choosing the Classification ModelClassification model types can be challenging because each model type has its own characteristic, which could be a strength or weakness depending on the problem.

For starters, you must answer a few questions about the type and purpose of data:Answering these questions can help narrow the choices and select the correct classification model.

Engineers and scientists can use cross-validation to test how accurately a model will evaluate data.

After cross-validation, you can select the best-fitting classification model.

There are many types of classification models, here are five common types:Engineers and scientists can simplify the decision-making process by using scalable software tools to determine which model best fits a set of features, assess classifier performance, compare and improve model accuracy, and finally, export the best model.

These tools also help users explore the data, select features, specify validation schemes, and train multiple models.

 Eliminating Data OverfittingOverfitting occurs when a model fits a particular dataset but does not generalize well to new data.

Overfitting is typically hard to avoid because it is often the result of insufficient training data, especially when the person responsible for the model did not gather the data.

The best way to avoid overfitting is by using enough training data to accurately reflect the model’s diversity and complexity.

Data regularization and generalization are two additional methods engineers and scientists can apply to check for overfitting.

Regularization is a technique that prevents the model from over-relying on individual data points.

Regularization algorithms introduce additional information into the model and handle multicollinearity and redundant predictors by making the model more parsimonious and accurate.

These algorithms typically work by applying a penalty for complexity, such as adding the coefficients of the model into the minimization or including a roughness penalty.

Generalization divides available data into three subsets.

The first set is the training set, and the second set is the validation set.

The error on the validation set is monitored during the training process, and the model is fine-tuned until accurate.

The third subset is the test set, which is used on the fully trained classifier after the training and cross-validation phases to test that the model hasn’t overfit the training and validation data.

There are six cross-validation methods that can help prevent overfitting:Machine learning veterans and beginners alike run into trouble with classification and overfitting.

While the challenges surrounding machine learning can seem daunting, leveraging the right tools and utilizing the validation methods covered here will help engineers and scientists apply machine learning more easily to real-world projects.

To learn more about classification modeling and overfitting and how MATLAB is helping to overcome this machine learning challenge, see the links below or email me at sdeland@mathworks.



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