Ask this to the ML Algos

To understand any machine learning algorithm in depth, I am proposing a list of some important questions that one must ask once read about it in depth. This will help you see the algorithm from 360 degrees.

• How this algorithm works?
• What are the algorithm’s assumptions? — Selective bias
• Is it useful for Classification or Regression or Clustering or other ML Task?
• Is Feature Scaling Required by this algorithm?
• How sensitive is this algorithm to missing values?
• Algorithm’s sensitivity towards Outliers?
• How to handle categorical, ordinal and numerical features in dataset?
• What optimisation technique is used to train the model?
• Is this algorithmic technique easily prone to Overfitting?
• What regularisations techniques are useful?
• What are the evaluation metrics for model performance?
• Is this Algorithmic model interpretable?

Keep Learning, Keep Hustling