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You're reading from  Python Data Mining Quick Start Guide

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Published inApr 2019
Reading LevelBeginner
PublisherPackt
ISBN-139781789800265
Edition1st Edition
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Nathan Greeneltch
Nathan Greeneltch
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Nathan Greeneltch

Nathan Greeneltch, PhD is a ML engineer at Intel Corp and resident data mining and analytics expert in the AI consulting group. Hes worked with Python analytics in both the start-up realm and the large-scale manufacturing sector over the course of the last decade. Nathan regularly mentors new hires and engineers fresh to the field of analytics, with impromptu chalk talks and division-wide knowledge-sharing sessions at Intel. In his past life, he was a physical chemist studying surface enhancement of the vibration signals of small molecules; a topic on which he wrote a doctoral thesis while at Northwestern University in Evanston, IL. Nathan hails from the southeastern United States, with family in equal parts from Arkansas and Florida
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Scikit-learn Estimator API

One of the reasons scikit-learn is so popular is its ease of use. There are only a few, well thought-out API designs in the library and they are applied in a sweeping manner across many different methods and routines. This chapter will make use of the Estimator API. It's extremely straightforward, and, once you understand how to use it, you can try our new regression and classification estimator methods with ease, because they all work in the same way (in other words, they all make use of the Estimator API).

The steps are given as follows:

  1. Import the module
  2. Instantiate the estimator object (regression or classification model in the following diagram)
  3. Fit the model-to-map input training data (X_train in the following diagram) to the ground truth y_train labels
  4. Predict y_pred on the new test data (X_test in the following diagram)

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Python Data Mining Quick Start Guide
Published in: Apr 2019Publisher: PacktISBN-13: 9781789800265

Author (1)

author image
Nathan Greeneltch

Nathan Greeneltch, PhD is a ML engineer at Intel Corp and resident data mining and analytics expert in the AI consulting group. Hes worked with Python analytics in both the start-up realm and the large-scale manufacturing sector over the course of the last decade. Nathan regularly mentors new hires and engineers fresh to the field of analytics, with impromptu chalk talks and division-wide knowledge-sharing sessions at Intel. In his past life, he was a physical chemist studying surface enhancement of the vibration signals of small molecules; a topic on which he wrote a doctoral thesis while at Northwestern University in Evanston, IL. Nathan hails from the southeastern United States, with family in equal parts from Arkansas and Florida
Read more about Nathan Greeneltch