Akurasi, sensitivitas, spesifisitas

yardstick dapat menghitung banyak metrik kinerja dari confusion matrix. Untuk regresi logistik, tiga metrik yang khususnya penting adalah: akurasi, sensitivitas, dan spesifisitas. Bisakah Anda mengidentifikasi arti masing-masing istilah tersebut dalam konteks model churn?

Latihan ini adalah bagian dari kursus

Pengantar Regresi di R

Latihan interaktif praktis

Ubah teori menjadi tindakan dengan salah satu latihan interaktif kami.

Mulai berolahraga

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()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

Latihan Saat Ini

Exercise 14: Selamat