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The Supervised Learning Workshop

You're reading from   The Supervised Learning Workshop Predict outcomes from data by building your own powerful predictive models with machine learning in Python

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781800209046
Length 532 pages
Edition 2nd Edition
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Authors (4):
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Blaine Bateman Blaine Bateman
Author Profile Icon Blaine Bateman
Blaine Bateman
Ashish Ranjan Jha Ashish Ranjan Jha
Author Profile Icon Ashish Ranjan Jha
Ashish Ranjan Jha
Ishita Mathur Ishita Mathur
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Ishita Mathur
Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
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Toc

Importing the Modules and Preparing Our Dataset

In the previous exercises and activities, we used terms such as Mean Absolute Error (MAE) and accuracy. In machine learning terms, these are called evaluation metrics and, in the next sections, we will discuss some useful evaluation metrics, what they are, and how and when to use them.

Note

Although this section is not positioned as an exercise, we encourage you to follow through this section carefully by executing the presented code. We will be using the code presented here in the upcoming exercises.

We will now load the data and models that we trained as part of Chapter 6, Ensemble Modeling. We will use the stacked linear regression model from Activity 6.01, Stacking with Standalone and Ensemble Algorithms, and the random forest classification model to predict the survival of passengers from Exercise 6.06, Building the Ensemble Model Using Random Forest.

First, we need to import the relevant libraries:

import pandas as...
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