Regresi logistik dengan glm()

Regresi linear dan regresi logistik adalah kasus khusus dari jenis model yang lebih luas yang disebut generalized linear models ("GLM"). Regresi linear berasumsi bahwa residual mengikuti distribusi Gaussian (normal). Sebaliknya, regresi logistik berasumsi bahwa residual mengikuti distribusi binomial.

Di sini, Anda akan memodelkan bagaimana lamanya hubungan dengan pelanggan memengaruhi churn.

churn tersedia.

Latihan ini adalah bagian dari kursus

Pengantar Regresi di R

Petunjuk latihan

Fit regresi logistik has_churned terhadap time_since_first_purchase menggunakan himpunan data churn. Simpan sebagai mdl_churn_vs_relationship.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Fit a logistic regression of churn vs. length of relationship using the churn dataset
mdl_churn_vs_relationship <- ___





# See the result
mdl_churn_vs_relationship

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Pengantar Regresi di R

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

You’ll learn the basics of this popular statistical model, what regression is, and how linear and logistic regressions differ. You’ll then learn how to fit simple linear regression models with numeric and categorical explanatory variables, and how to describe the relationship between the response and explanatory variables using model coefficients.

Exercise 1: A tale of two variables Exercise 2: Which one is the response variable?Exercise 3: Visualizing two variables Exercise 4: Fitting a linear regression Exercise 5: Estimate the intercept Exercise 6: Estimate the slope Exercise 7: Linear regression with lm()Exercise 8: Categorical explanatory variables Exercise 9: Visualizing numeric vs. categorical Exercise 10: Calculating means by category Exercise 11: lm() with a categorical explanatory variable

In this chapter, you’ll discover how to use linear regression models to make predictions on Taiwanese house prices and Facebook advert clicks. You’ll also grow your regression skills as you get hands-on with model objects, understand the concept of "regression to the mean", and learn how to transform variables in a dataset.

Exercise 1: Making predictions Exercise 2: Predicting house prices Exercise 3: Visualizing predictions Exercise 4: The limits of prediction Exercise 5: Working with model objects Exercise 6: Extracting model elements Exercise 7: Manually predicting house prices Exercise 8: Using broom Exercise 9: Regression to the mean Exercise 10: Home run!Exercise 11: Plotting consecutive portfolio returns Exercise 12: Modeling consecutive returns Exercise 13: Transforming variables Exercise 14: Transforming the explanatory variable Exercise 15: Transforming the response variable too

In this chapter, you’ll learn how to ask questions of your model to assess fit. You’ll learn how to quantify how well a linear regression model fits, diagnose model problems using visualizations, and understand the leverage and influence of each observation used to create the model.

Exercise 1: Quantifying model fit Exercise 2: Coefficient of determination Exercise 3: Residual standard error Exercise 4: Visualizing model fit Exercise 5: Residuals vs. fitted values Exercise 6: Q-Q plot of residuals Exercise 7: Scale-location Exercise 8: Drawing diagnostic plots Exercise 9: Outliers, leverage, and influence Exercise 10: Leverage Exercise 11: Influence Exercise 12: Extracting leverage and influence

Learn to fit logistic regression models. Using real-world data, you’ll predict the likelihood of a customer closing their bank account as probabilities of success and odds ratios, and quantify model performance using confusion matrices.

Exercise 1: Mengapa Anda memerlukan regresi logistik Exercise 2: Mengeksplorasi variabel penjelas Exercise 3: Memvisualisasikan model linear dan logistik Exercise 4: Regresi logistik dengan glm()

Latihan Saat Ini

Exercise 5: Prediksi dan rasio odds Exercise 6: Probabilitas Exercise 7: Hasil yang paling mungkin Exercise 8: Rasio odds Exercise 9: Log odds ratio Exercise 10: Mengukur kecocokan regresi logistik Exercise 11: Menghitung confusion matrix Exercise 12: Mengukur kinerja model logistik Exercise 13: Akurasi, sensitivitas, spesifisitas Exercise 14: Selamat