Getting to learn

Building a machine learning system comes with some challenges and issues; we will try to address them in this section. Many of these issues are domain specific and others aren't.

Challenges of learning

The following is an overview of the challenges and issues that you will typically face when trying to build a learning system.

Feature extraction – feature engineering

Feature extraction is one of the crucial steps toward building a learning system. If you did a good job in this challenge by selecting the proper/right number of features, then the rest of the learning process will be easy. Also, feature extraction is domain dependent and it requires prior knowledge to have a sense of what features could be important for a particular task. For example, the features for our fish recognition system will be different from the ones for spam detection or identifying fingerprints.

The feature extraction step starts from the raw data that you have. Then build derived variables/values (features) that are informative about the learning task and facilitate the next steps of learning and evaluation (generalization).

Some tasks will have a vast number of features and fewer training samples (observations) to facilitate the subsequent learning and generalization processes. In such cases, data scientists use dimensionality reduction techniques to reduce the vast number of features to a smaller set.

Noise

In the fish recognition task, you can see that the length, weight, fish color, as well as the boat color may vary, and there could be shadows, images with low resolution, and other objects in the image. All these issues affect the significance of the proposed explanatory features that should be informative about our fish classification task.

Work-arounds will be helpful in this case. For example, someone might think of detecting the boat ID and mask out certain parts of the boat that most likely won't contain any fish to be detected by our system. This work-around will limit our search space.

Overfitting

As we have seen in our fish recognition task, we have tried to enhance our model's performance by increasing the model complexity and perfectly classifying every single instance of the training samples. As we will see later, such models do not work over unseen data (such as the data that we will use for testing the performance of our model). Having trained models that work perfectly over the training samples but fail to perform well over the testing samples is called overfitting.

If you sift through the latter part of the chapter, we build a learning system with an objective to use the training samples as a knowledge base for our model in order to learn from it and generalize over the unseen data. Performance error of the trained model is of no interest to us over the training data; rather, we are interested in the performance (generalization) error of the trained model over the testing samples that haven't been involved in the training phase.

Selection of a machine learning algorithm

Sometimes you are unsatisfied with the execution of the model that you have utilized for a particular errand and you need an alternate class of models. Each learning strategy has its own presumptions about the information it will utilize as a learning base. As an information researcher, you have to discover which suspicions will fit your information best; by this you will have the capacity to acknowledge to attempt a class of models and reject another.

Prior knowledge

As discussed in the concepts of model selection and feature extraction, the two issues can be dealt with, if you have prior knowledge about:

• The appropriate feature
• Model selection parts

Having prior knowledge of the explanatory features in the fish recognition system enabled us to differentiate amid different types of fish. We can go promote by endeavoring to envision our information and get some feeling of the information types of the distinctive fish classifications. On the basis of this prior knowledge, apt family of models can be chosen.

Missing values

Missing features mainly occur because of a lack of data or choosing the prefer-not-to-tell option. How can we handle such a case in the learning process? For example, imagine we find the width of specific a fish type is missing for some reason. There are many ways to handle these missing features.