courses
Python으로 배우는 예측 분석 입문
기초적인숙련도 수준
업데이트됨 2022. 11.PythonMachine Learning414 videos52 exercises4,100 XP22,217성과 증명서
수천 개의 회사에서 학습자들에게 사랑받는 제품입니다.
2명 이상을 교육하시나요?
DataCamp for Business 사용해 보세요강좌 설명
필수 조건
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.