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Python으로 배우는 예측 분석 입문

기초기술 수준

업데이트됨 2022. 11.

이 과정에서는 예측을 위해 로지스틱 회귀 모델을 사용하고 효과적으로 제시하는 방법을 학습합니다.

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이 강의에서는 의미 있는 변수를 사용해 로지스틱 회귀 모델을 구축하는 방법을 배웁니다. 또한 이 모델로 예측을 수행하고, 모델과 성능을 비즈니스 이해관계자에게 효과적으로 전달하는 방법도 익힙니다.

선수 조건

Intermediate Python

1

Building Logistic Regression Models

In this Chapter, you'll learn the basics of logistic regression: how can you predict a binary target with continuous variables and, how should you interpret this model and use it to make predictions for new examples?

Introduction and base table structure

Structure of the base table

Exploring the base table

Exploring the predictive variables

Logistic regression

Interpretation of coefficients

Building a logistic regression model

Showing the coefficients and intercept

Using the logistic regression model

Making predictions

Donor that is most likely to donate

2

Forward stepwise variable selection for logistic regression

In this chapter you'll learn why variable selection is crucial for building a useful model. You'll also learn how to implement forward stepwise variable selection for logistic regression and how to decide on the number of variables to include in your final model.

Motivation for variable selection

Which model is best?

Calculating AUC

Using different sets of variables

Forward stepwise variable selection

Selecting the next best variable

Finding the order of variables

Correlated variables

Deciding on the number of variables

Partitioning

Evaluating a model on test and train

Building the AUC curves

Deciding the cut-off

3

Explaining model performance to business

Now that you know how to build a good model, you should convince stakeholders to use it by creating appropriate graphs. You will learn how to construct and interpret the cumulative gains curve and lift graph.

The cumulative gains curve

Interpreting the cumulative gains curve

Constructing the cumulative gains curve

A random model

The lift curve

Interpreting the lift curve

Constructing the lift curve

A perfect model

Guiding business to better decisions

Targeting using cumulative gains curve

Business case using lift curve

Business case using cumulative gains curve

4

Interpreting and explaining models

In a business context, it is often important to explain the intuition behind the model you built. Indeed, if the model and its variables do not make sense, the model might not be used. In this chapter you'll learn how to explain the relationship between the variables in the model and the target by means of predictor insight graphs.

Predictor insight graphs

Interpretation of predictor insight graphs

Retrieving information from the predictor insight table

Discretization of continuous variables

Discretization of a certain variable

Discretizing all variables

Making clean cuts

Preparing the predictor insight graph table

Calculating average incidences

Constructing the predictor insight graph table

Grouping all predictor insight graph tables

Plotting the predictor insight graph

Plotting the incidences

Plotting the group sizes

Putting it all together

Python으로 배우는 예측 분석 입문

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