Grundlagen robusten maschinellen Lernens: Umgang mit Ausreißern und Anomalien in...
90,52 €
There are two main approaches described in th: using outlier-tolerant ML tools, or removing outliers before using conventional tools. Balancing theoretical foundations with practical Python code, it provides all the necessary skills to enhance the accuracy, stability and reliability of ML models.
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