DATA SCIENCE: Advanced Method And Strategies To Learn Data Science For Business by William Vance

Author:William Vance [Vance, William]
Language: eng
Format: azw3
Published: 2020-03-18T16:00:00+00:00

Chapter Seven: Classification Modeling at a Glance

For every data scientist, classification modeling is an essential tool that should be utilized. This is because they use classification modeling in performing predictive analytics. Classification is another essential machine learning tool that is supervised, which makes useful inferences from labeled data. Data labels facilitate decision making for your models depending on the set of logic rules and deductions you have defined. Similar to the k-means approach, a clustering algorithm can help you make predictions on subgroups using your unlabeled datasets. We've explored the concepts of machine learning in both the primary and advanced versions of the book, but this time, I think it's time to take things a little further by delving a little deeper into machine learning algorithms based on a useful case study.

Instance-based learning classifiers are supervised, lazy learners — they have no period of preparation, and they merely memorize in-memory training data to predict classifications for new data points. A method of classifier looks at instances — observations within a dataset — and the classifier searches for observations that are most similar for each new observation and then classifies the new observation based on its similarity to the instances in the training set. Most classifiers based on instances and useful deductions may include the following:

K-nearest neighbor, often written as the kNN.

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