Error analysis helps you understand which part to work on to decrease those errors and what to prioritize


University of Michigan - Ann Arbor

•	Data leakage

•	Modeling non-linear data with linear models 

•	Forgetting to regularize data if needed

•	Substandard data cleaning

•	Failing to understand the underlying logic behind certain packages

Common errors in machine learning

https://www.coursera.org/learn/machine-learning-projects?specialization=deep-learning

Structuring Machine Learning Projects

https://www.mygreatlearning.com/blog/5-common-mistakes-while-working-with-machine-learning-algorithms/

Common errors in machine learning source

Data leakage is when information that should not be in the training set is included. This data includes information that a model would not have access to in the future and this leads to a model that is overly accurate for the training data, but will not be nearly as effective once it is tested. Data leakage will become apparent if the training model is much better than the test results and to prevent data leakage, be aware of the data that is included in the training data.

Data leakage

Linear data is data that follows a successive pattern whereas non-linear data follows a hierarchical pattern. This is an important concept as linear data and non-linear data structures are compatible with different types of machine learning algorithms, and if the wrong algorithms are chosen, the learned model will not effectively predict the data. 

Linear versus non-linear data

Data cleaning involves removing errors from data sets and this is extremely important as models follow the “garbage in garbage out” principle in which erroneous data leads to erroneous conclusions. In order to identify errors in the datasets, a variety of rules, patterns, statistical techniques, and more can be used. Common errors include incomplete rows, duplicate rows, incorrectly labeled data, and more.

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