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The Data Science Workshop

The Data Science Workshop

By : Anthony So , Thomas Joseph, Robert Thas John, Andrew Worsley , Dr. Samuel Asare
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The Data Science Workshop

The Data Science Workshop

3 (2)
By: Anthony So , Thomas Joseph, Robert Thas John, Andrew Worsley , Dr. Samuel Asare

Overview of this book

Where there’s data, there’s insight. With so much data being generated, there is immense scope to extract meaningful information that’ll boost business productivity and profitability. By learning to convert raw data into game-changing insights, you’ll open new career paths and opportunities. The Data Science Workshop begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. You’ll learn to select a relevant metric and even assess the performance of your model. To tune the hyperparameters of an algorithm and improve its accuracy, you’ll get hands-on with approaches such as grid search and random search. Next, you’ll learn dimensionality reduction techniques to easily handle many variables at once, before exploring how to use model ensembling techniques and create new features to enhance model performance. In a bid to help you automatically create new features that improve your model, the book demonstrates how to use the automated feature engineering tool. You’ll also understand how to use the orchestration and scheduling workflow to deploy machine learning models in batch. By the end of this book, you’ll have the skills to start working on data science projects confidently. By the end of this book, you’ll have the skills to start working on data science projects confidently.
Table of Contents (16 chapters)
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Preface
12
12. Feature Engineering

Local Interpretation with LIME

After training our model, we usually use it for predicting outcomes on unseen data. The global interpretations we saw earlier, such as model coefficient, variable importance, and the partial dependence plot, gave us a lot of information on the features at an overall level. Sometimes we want to understand what has influenced the model for a specific case to predict a specific outcome. For instance, if your model is to assess the risk of offering credit to a new client, you may want to understand why it rejected the case for a specific lead. This is what local interpretation is for: analyzing a single observation and understanding the rationale behind the model's decision. In this section, we will introduce you to a technique called Locally Interpretable Model-Agnostic Explanations (LIME).

If we are using a linear model, it is extremely easy to understand the contribution of each variable to the predicted outcome. We just need to look at the coefficients...

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