As the kNN algorithm literally "learns by example" it is a case in point for starting to understand supervised machine learning. This chapter will introduce classification while working through the application of kNN to self-driving vehicle road sign recognition.

Classification with Nearest Neighbors

Recognizing a road sign with kNN

Thinking like kNN

Exploring the traffic sign dataset

Classifying a collection of road signs

What about the 'k' in kNN?

Understanding the impact of 'k'

Testing other 'k' values

Seeing how the neighbors voted

Data preparation for kNN

Why normalize data?

k-Nearest Neighbors (kNN)

Naive Bayes uses principles from the field of statistics to make predictions. This chapter will introduce the basics of Bayesian methods while exploring how to apply these techniques to iPhone-like destination suggestions.

Understanding Bayesian methods

Computing probabilities

Understanding dependent events

A simple Naive Bayes location model

Examining "raw" probabilities

Understanding independence

Understanding NB's "naivety"

Who are you calling naive?

A more sophisticated location model

Preparing for unforeseen circumstances

Understanding the Laplace correction

Applying Naive Bayes to other problems

Handling numeric predictors

Naive Bayes

Logistic regression involves fitting a curve to numeric data to make predictions about binary events. Arguably one of the most widely used machine learning methods, this chapter will provide an overview of the technique while illustrating how to apply it to fundraising data.

Membuat prediksi biner dengan regresi

Membangun model regresi logistik sederhana

Membuat prediksi biner

Keterbatasan akurasi

Pertukaran kinerja model

Menghitung Kurva ROC dan AUC

Membandingkan kurva ROC

Variabel semu, data hilang, dan interaksi

Mengode fitur kategorikal

Menangani data hilang

Memahami indikator nilai hilang

Membangun model yang lebih canggih

Pemilihan fitur otomatis

Bahaya stepwise regression

Membangun model regresi stepwise

Logistic Regression

Classification trees use flowchart-like structures to make decisions. Because humans can readily understand these tree structures, classification trees are useful when transparency is needed, such as in loan approval. We'll use the Lending Club dataset to simulate this scenario.

Making decisions with trees

Building a simple decision tree

Visualizing classification trees

Understanding the tree's decisions

Growing larger classification trees

Why do some branches split?

Creating random test datasets

Building and evaluating a larger tree

Conducting a fair performance evaluation

Tending to classification trees

Preventing overgrown trees

Creating a nicely pruned tree

Why do trees benefit from pruning?

Seeing the forest from the trees

Understanding random forests

Building a random forest model

Classification Trees

Lending Club loan data

Traffic sign image data

Donation data

Brett's location data

Pengantar Machine Learning tingkat pemula ini membahas empat algoritma klasifikasi yang paling umum. Anda akan memperoleh pemahaman dasar tentang cara setiap algoritma menyelesaikan tugas pembelajaran, serta mempelajari fungsi-fungsi R yang diperlukan untuk menerapkan alat ini pada pekerjaan Anda sendiri.

Intermediate R

Dalam kursus ini, Anda akan belajar dasar-dasar pembelajaran mesin untuk klasifikasi.

Supervised Learning di R: Klasifikasi

Di kursus ini Anda akan mempelajari dasar-dasar machine learning untuk klasifikasi.

Associate Data Scientist di R

Dasar-Dasar Pembelajaran Mesin di R

Ilmuwan Pembelajaran Mesin di R

Membandingkan kurva ROC

Supervised Learning di R: Klasifikasi

Latihan interaktif langsung