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Python で学ぶ Machine Learning のための特徴量エンジニアリング

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更新日 2023/02

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PythonMachine Learning

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コース内容

日々、最新の Machine Learning の応用が世界を変えているというニュースを目にします。しかし、その多くは、これらの高度なモデルを使う前に膨大なデータ整形と特徴量エンジニアリングが必要になる事実を見落としがちです。本コースでは、まさにその実践方法を学びます。Stack Overflow Developers の調査データや、米国大統領就任演説のアーカイブを扱い、カテゴリカル、連続値、非構造化データから最適に前処理と特徴量設計を行う方法を理解します。どのようなデータでも自分の Machine Learning モデルに備えるための、実践的なスキルを身につけていただけます。

前提条件

Supervised Learning with scikit-learn

1

Creating Features

In this chapter, you will explore what feature engineering is and how to get started with applying it to real-world data. You will load, explore and visualize a survey response dataset, and in doing so you will learn about its underlying data types and why they have an influence on how you should engineer your features. Using the pandas package you will create new features from both categorical and continuous columns.

Why generate features?

Getting to know your data

Selecting specific data types

Dealing with categorical features

One-hot encoding and dummy variables

Dealing with uncommon categories

Numeric variables

Binarizing columns

Binning values

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2

Dealing with Messy Data

This chapter introduces you to the reality of messy and incomplete data. You will learn how to find where your data has missing values and explore multiple approaches on how to deal with them. You will also use string manipulation techniques to deal with unwanted characters in your dataset.

Why do missing values exist?

How sparse is my data?

Finding the missing values

Dealing with missing values (I)

Listwise deletion

Replacing missing values with constants

Dealing with missing values (II)

Filling continuous missing values

Imputing values in predictive models

Dealing with other data issues

Dealing with stray characters (I)

Dealing with stray characters (II)

Method chaining

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3

Conforming to Statistical Assumptions

In this chapter, you will focus on analyzing the underlying distribution of your data and whether it will impact your machine learning pipeline. You will learn how to deal with skewed data and situations where outliers may be negatively impacting your analysis.

Data distributions

What does your data look like? (I)

What does your data look like? (II)

When don't you have to transform your data?

Scaling and transformations

Normalization

Standardization

Log transformation

When can you use normalization?

Removing outliers

Percentage based outlier removal

Statistical outlier removal

Scaling and transforming new data

Train and testing transformations (I)

Train and testing transformations (II)

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4

Dealing with Text Data

Finally, in this chapter, you will work with unstructured text data, understanding ways in which you can engineer columnar features out of a text corpus. You will compare how different approaches may impact how much context is being extracted from a text, and how to balance the need for context, without too many features being created.

Encoding text

Cleaning up your text

High level text features

Word counts

Counting words (I)

Counting words (II)

Limiting your features

Text to DataFrame

Term frequency-inverse document frequency

Inspecting Tf-idf values

Transforming unseen data

Using longer n-grams

Finding the most common words

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Python で学ぶ Machine Learning のための特徴量エンジニアリング

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