Performance Evaluation of Classification Models Evaluation of Classification models during bank marketing campaign under supervised machine learning domain
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- Englisch ausgewählt
Fr. 74.90
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Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
20.09.2019
Verlag
LAP LAMBERT Academic PublishingSeitenzahl
96
Maße (L/B/H)
22/15/0.7 cm
Gewicht
161 g
Auflage
1. Auflage
Sprache
Englisch
ISBN
978-620-0-31986-9
As the amount of data is increasing over the tremendous rate, it is extremely viable to imply smart analysis. It deals with optimization of performance criterion dealing with examples relevant to present and past situations. Learning plays a vital role in making predictions from analysis of data set properties. We also ensure that the selected data set suits our purpose of predicting futuristic events and unseen samples. However while dealing with problems of classification in machine learning we need to determine and draw observations relevant to a problem statement having disjoint set of training data. Mining information from data enrols classification, clustering and other such methodologies as its subsets. The thesis presents a classical descriptive procedure to compare various classification schemes under single roof and draw analysis over the best scorer in terms of accuracy to draw predictions of Bank Marketing success rate while targeting potential customers. Various data sets can be filtered by the approached schemes to make decision during binary and multi valued classification. However the classification model ranks one over other to determine the best fit choice.
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