From optimising workforces to projecting consumer demand, enterprises are increasingly adapting and building smart Machine Learning modules. However, many an organisations are struggling to put in best machine learning practices and are missing on results.This article lists down 8 tips for building effective machine learning modules for organisations.
1. Look & Slice
Review your data before starting to build a machine learning algorithm around it. Ensure running Head or Tail commands in Python to touch all observations in the data sets to ensure you have the right data. Structure your data by slicing in individual categories and then build individual ML modules around them.
2. Use simple models
Using simple models with regression and decision trees (white-box models) makes the process of deploying ML models easy and are also easily explainable to individual stakeholders.
3. Identify rare events
Correctly classifying rare events is a must for any ML module to be effective. It is difficult but can be achieved by oversampling or undersampling of a biased training data set or simply sing decision processing.
4. Identify rare events
To ensure that the constructed modules are in line with thorough objectives of the data mining process, else deployment can take numerous days and some modules might not even deploy. Ensure that standard analytical data marts are in place to catalyse data reuse.
Assign hyperparameters before you start building a ML Model. It helps in autotuning for accurate results and makes sure that the module is optimised (guided by performance metrics).
6. High Bias / High Variance
Create a model that balances well between high bias and high variance data. It helps the model to still run on new data inputs. Choose your metric based on the type of data results received.