little to no manual intervention. However, feature engineering, an arguably more valua…g the best model for a dataset with little to no manual intervention. However, feature engineering, an arguably more valuable aspect of the machine learning pipeline, remains almost entirely a human labor.
If you’re coming from Excel, then your head is in the right place. Both Dash and Excel use a “reactive” programming model. In Excel, output cells update automatically when input cells change. Any cell can be an output, an input, or both. Input cells aren’t aware of which output cells depend on them, making it easy to add new output cells or chain together a series of cells. Here’s an example Excel “application”:
iers …get values. But if we try to minimize MAE, that prediction would be the median of all observations. We know that median is more robust to outliers than mean, which consequently makes MAE more robust to outliers than MSE.
cessing and ana…idual jobs, and usually because current incarnation of the system doesn’t allow for simple scaling. Also note that people who write data pipelines typically are not software engineers, and their mission and competencies are centered around processing and analyzing data, not building workflow management systems.
…: What this form of organization presupposes is VW > Acura > Honda based on the categorical values. Say supposing your model internally calculates average, then accordingly we get, 1+3 = 4/2 =2. This implies that: Average of VW and Honda is Acura. This is definitely a recipe for disaster. This model’s prediction would have a lot of errors.