Jelajahi data gender

Data gender memuat indeks Weight, Height, dan BMI dari 10.000 orang. Data asli memiliki label Gender untuk 5.000 orang yang mengidentifikasi diri sebagai perempuan dan 5.000 lainnya sebagai laki-laki. Label ini akan berguna nanti untuk menguji seberapa baik kinerja pengelompokan dibandingkan label sebenarnya. Namun, dalam subset himpunan data ini, label tidak disertakan.

Data gender_with_probs juga memuat probabilitas setiap titik data termasuk ke dalam suatu cluster. Karena kita tertarik pada dua cluster, probabilitas mendekati 1 mengacu pada satu cluster dan mendekati 0 pada cluster lainnya.

Tujuan latihan ini adalah agar Anda mendapat gambaran tentang seperti apa tampilan himpunan data pengelompokan yang khas sebelum dan sesudah pengelompokan.

Latihan ini adalah bagian dari kursus

Model Campuran di R

Petunjuk latihan

Gunakan fungsi head untuk melihat 6 observasi pertama dari gender.
Gunakan fungsi head untuk melihat 6 observasi pertama dari gender_with_probs.
Buat scatterplot dengan Weight pada sumbu x dan BMI pada sumbu y. Warnai titik berdasarkan probabilitasnya.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Have a look to gender (before clustering)
head(___)

# Have a look to gender_with_probs (after clustering)
head(___)

# Scatterplot with probabilities
gender_with_probs %>% 
  ggplot(aes(x = ___, y = ___, col = ___))+
  geom_point(alpha = 0.5)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Model Campuran di R

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

In this chapter, you will be introduced to fundamental concepts in model-based clustering and how this approach differs from other clustering techniques. You will learn the generating process of Gaussian Mixture Models as well as how to visualize the clusters.

Exercise 1: Pengantar pengelompokan berbasis model Exercise 2: Pendekatan pengelompokan (clustering)Exercise 3: Jelajahi data gender

Latihan Saat Ini

Exercise 4: Distribusi Gaussian Exercise 5: Sampling a Gaussian distribution Exercise 6: (kurang baik) Estimasi mean dan sd Exercise 7: Model campuran Gaussian (GMM)Exercise 8: Mensimulasikan campuran dua distribusi Gaussian Exercise 9: Plot histogram Campuran Gaussian Exercise 10: Campuran tiga distribusi Gaussian

In this chapter, you will be introduced to the main structure of Mixture Models, how to address different data with this approach and how to estimate the parameters involved. To accomplish the estimation, you will learn an iterative method called Expectation-Maximization algorithm.

Exercise 1: Structure of mixture models Exercise 2: Which probability distribution?Exercise 3: Handwritten digits dataset Exercise 4: Parameters estimation Exercise 5: Estimation given the probabilities Exercise 6: Calculating the probabilities Exercise 7: EM algorithm Exercise 8: Expectation function Exercise 9: Maximization function Exercise 10: Apply the two steps Exercise 11: Plot the estimated clusters

This chapter shows how to fit Gaussian Mixture Models in 1 and 2 dimensions with `flexmix` package. The data used is formed by 10.000 observations of people with their weight, height, body mass index and informed gender.

Exercise 1: Univariate Gaussian Mixture Models Exercise 2: Number of clusters Exercise 3: Number of parameters Exercise 4: Univariate Gaussian Mixture Models with flexmix Exercise 5: Univariate case with flexmix Exercise 6: Extracting Parameters for Univariate Case Exercise 7: Visualizing Univariate Gaussian Mixture Model Exercise 8: Compare the results Exercise 9: Bivariate Gaussian Mixture Models Exercise 10: Cross-term from covariance matrix Exercise 11: Parameters in the bivariate case Exercise 12: Bivariate Gaussian Mixture Models with flexmix Exercise 13: Fit the model with cross-terms Exercise 14: Get the components Exercise 15: Create the ellipses Exercise 16: Visualize the clusters

In this module, you will learn how Mixture Models extends to consider probability distributions different from the Gaussian and how these models are fitted with `flexmix`. The datasets used are handwritten digits images and the number of crimes in Chicago city. For the first dataset you will find clusters that summarize the handwritten digits and for the second dataset, you will find clusters of communities where is more or less dangerous to live in.

Exercise 1: Bernoulli Mixture Models Exercise 2: Binary images Exercise 3: How many values?Exercise 4: Bernoulli Mixture Models with flexmix Exercise 5: Handwritten digits with `flexmix`Exercise 6: Poisson Mixture Models Exercise 7: Discover the lambda Exercise 8: Sample from Poisson distribution Exercise 9: Poisson Mixture Models with flexmix Exercise 10: Crimes data with `flexmix`