As described last week, the Scikit-learn chi-square feature selection is not usable until the bug #21455 is addressed. The problem concerns sklearn.feature_selection.chi2 and the derivative methods, including SelectKBest, if used for categorical features other than binary. The nature of the
Your model may be inaccurate
With Machine Learning in Python, you may do feature selection with SelectKBest. As I just confirmed, this method sometimes returns faulty results. This potentially impacts the accuracy of numerous ML models worldwide. Below the details and the way out. The
Answering Why (with Chi-Square)
Analysts don’t like the “why” questions. They are tough to answer. For instance, in a help desk analysis, it is easy to show which tickets are resolved faster. But it is difficult to say why. In my practice in Sopra
What makes Data Quality so difficult
Garbage in, garbage out. Analysis of untrusted or poorly understood data will yield incorrect results. Hence the textbook approach is to clean the data first, and only then proceed with data analytics. For instance, in the data lakes, the data