课程
Introduction to Predictive Analytics in Python
基础技能水平
更新时间 2022年11月
PythonMachine Learning4小时14 视频52 道练习4,100 XP22,570成就证明
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先决条件
Intermediate Python1
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?
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.
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.
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.
Introduction to Predictive Analytics in Python
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