Regular math/s vs ML

Course Q&A Machine Learning Specialization Supervised ML: Regression and Classification
2

Hi there,

it sounds like you used all the data (100% training data) to fit the model and then went for extrapolation. A good practice in ML is to use:

  • a training set to fit the data (e.g. 70% or so)
  • a validation set to tune hyperpameters or improve features
  • a final test set, which was never seen by the model as final litmus test before deploying

Here you can find a nice outline from Prof. Andrew Ng:

Note: ML is a highly iterative process (just to give you one reason: because in reality you often will observe a distribution shift of data) and often the CRISP-DM method is used to work from the business problem iteratively through several steps to deployment and operation to realise the actual benefit in a data-driven way.

To fit the model you do not necessarily need gradient descent. There are several other ways to solve the optimization problem to obtain your model parameters. E.g. in a linear regression you can calculate them analytically.

Best
Christian

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