Mencocokkan model logistik

Universitas yang meneliti hubungan antara jam belajar dan hasil pada suatu tes memberikan kepada Anda himpunan data yang berisi jumlah jam belajar mahasiswa serta apakah mereka gagal atau lulus tes, dan meminta Anda untuk mencocokkan model guna memprediksi kinerja di masa mendatang.

Data tersedia dalam variabel hours_of_study dan outcomes. Gunakan data ini untuk mencocokkan model LogisticRegression. numpy telah diimpor sebagai np untuk memudahkan Anda.

Latihan ini adalah bagian dari kursus

Dasar-Dasar Probabilitas di Python

Petunjuk latihan

Impor LogisticRegression dari sklearn.linear_model.
Buat model menggunakan LogisticRegression(C=1e9).
Masukkan data ke metode model.fit().
Buat variabel untuk setiap parameter, tetapkan nilai dari model, lalu cetak parameter beta1 dan beta0.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Import LogisticRegression
from sklearn.linear_model import ____

# sklearn logistic model
model = ____(C=1e9)
model.____(____, ____)

# Get parameters
beta1 = model.coef_[0][0]
beta0 = model.intercept_[0]

# Print parameters
print(____, ____)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Dasar-Dasar Probabilitas di Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

A coin flip is the classic example of a random experiment. The possible outcomes are heads or tails. This type of experiment, known as a Bernoulli or binomial trial, allows us to study problems with two possible outcomes, like “yes” or “no” and “vote” or “no vote.” This chapter introduces Bernoulli experiments, binomial distributions to model multiple Bernoulli trials, and probability simulations with the scipy library.

Exercise 1: Let’s flip a coin in Python Exercise 2: Flipping coins Exercise 3: Using binom to flip even more coins Exercise 4: Probability mass and distribution functions Exercise 5: Predicting the probability of defects Exercise 6: Predicting employment status Exercise 7: Predicting burglary conviction rate Exercise 8: Expected value, mean, and variance Exercise 9: Calculating the expected value and variance Exercise 10: Calculating the sample mean Exercise 11: Checking the result Exercise 12: Calculating the mean and variance of a sample

In this chapter you'll learn to calculate various kinds of probabilities, such as the probability of the intersection of two events and the sum of probabilities of two events, and to simulate those situations. You'll also learn about conditional probability and how to apply Bayes' rule.

Exercise 1: Calculating probabilities of two events Exercise 2: Any overlap?Exercise 3: Measuring a sample Exercise 4: Joint probabilities Exercise 5: Deck of cards Exercise 6: Conditional probabilities Exercise 7: Delayed flights Exercise 8: Contingency table Exercise 9: More cards Exercise 10: Total probability law Exercise 11: Formula 1 engines Exercise 12: Voters Exercise 13: Bayes' rule Exercise 14: Conditioning Exercise 15: Factories and parts Exercise 16: Swine flu blood test

Until now we've been working with binomial distributions, but there are many probability distributions a random variable can take. In this chapter we'll introduce three more that are related to the binomial distribution: the normal, Poisson, and geometric distributions.

Exercise 1: Normal distributions Exercise 2: Range of values Exercise 3: Plotting normal distributions Exercise 4: Within three standard deviations Exercise 5: Normal probabilities Exercise 6: Restaurant spending example Exercise 7: Smartphone battery example Exercise 8: Adults' heights example Exercise 9: Poisson distributions Exercise 10: ATM example Exercise 11: Highway accidents example Exercise 12: Generating and plotting Poisson distributions Exercise 13: Geometric distributions Exercise 14: Catching salmon example Exercise 15: Free throws example Exercise 16: Generating and plotting geometric distributions

No that you know how to calculate probabilities and important properties of probability distributions, we'll introduce two important results: the law of large numbers and the central limit theorem. This will expand your understanding on how the sample mean converges to the population mean as more data is available and how the sum of random variables behaves under certain conditions. We will also explore connections between linear and logistic regressions as applications of probability and statistics in data science.

Exercise 1: Dari rataan sampel ke rataan populasi Exercise 2: Menghasilkan sebuah sampel Exercise 3: Menghitung mean sampel Exercise 4: Memplot rata-rata sampel Exercise 5: Menjumlahkan peubah acak Exercise 6: Rata-rata sampel Exercise 7: Rata-rata sampel mengikuti distribusi normal Exercise 8: Menjumlahkan lemparan dadu Exercise 9: Regresi linear Exercise 10: Mencocokkan model Exercise 11: Memprediksi nilai ujian Exercise 12: Mempelajari residual Exercise 13: Regresi logistik Exercise 14: Mencocokkan model logistik

Latihan Saat Ini

Exercise 15: Memprediksi apakah siswa akan lulus Exercise 16: Lulus dua ujian Exercise 17: Menutup pembahasan