What are rules in machine learning?
Rule #1: Don’t be afraid to launch a product without machine learning. Machine learning is cool, but it requires data. Theoretically, you can take data from a different problem and then tweak the model for a new product, but this will likely underperform basic heuristics.
What is a decision rule in ML?
A decision rule is a simple IF-THEN statement consisting of a condition (also called antecedent) and a prediction. For example: IF it rains today AND if it is April (condition), THEN it will rain tomorrow (prediction). A single decision rule or a combination of several rules can be used to make predictions.
Are there any rules for using machine learning?
Rule #1: Don’t be afraid to launch a product without machine learning. Machine learning is cool, but it requires data. Theoretically, you can take data from a different problem and then tweak the model for a new product, but this will likely underperform basic heuristics.
How to choose the best machine learning model?
Selection of the best model is made on the basis of the model’s performance on the testing set and in efforts to obtain the best possible model, hyperparameter optimization may also be performed. Another common approach for data splitting is to split the data to 3 portions: (1) training set, (2) validation set and (3) testing set.
How is a training set used in machine learning?
Similar to what was explained above, the training set is used to build a predictive model and is also evaluated on the validation set whereby predictions are made, model tuning can be made (e.g. hyperparameter optimization) and selection of the best performing model based on results of the validation set.
What are the acceptance criteria in machine learning?
The acceptance criteria are one of the drivers of your backlog (Jupyter Notebook or python module, docker or local deployment, project log, model artifact, model predictions, etc).