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
Practical AIOps: 5 use cases
In Sopra Steria we manage the IT infrastructure and applications of big clients. We process millions of service tickets and infrastructure events. This massive stream of data comes from monitoring tools such as Zabbix, Nagios, Solarwinds, and higher level frameworks:
When Accuracy Grows But Precision Falls
My Machine Learning classifier’s prediction accuracy improves with the growing volume of train data. But at the same time, its precision falls. Why so? And how to fix it? Read on. reducing the problem to classification At Sopra Steria, we