Explainability in Data Science:- Data, Model & Prediction
XAI( Explainable AI ) is grabbing lime-light in machine learning. How can we be sure that image classification algo is learning faces not background ? Customer wants to know why loan is disapproved? Globally important variable might not be responsible/ imp for individual prediction. Here XAI comes to rescue- We have taken data from classification_data This has some sensor values and an output class. A) Data Explainability - what are the basic understanding required from data perspective. 1) Identify missing values, co-linear feature, feature interaction, zero importance feature, low important feature, single value feature and handle missing values, remove/ handle features accordingly. 2) Missing values- no missing values from data description 3) No good correlation between variables- can be seen from correlation plots 4) ...