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Kursus

Klasifikator Linear di Python

MenengahTingkat Keterampilan

Diperbarui 10/2023

Dalam kursus ini, Anda akan mempelajari detail tentang klasifikasi linier seperti regresi logistik dan SVM.

Mulai Kursus Gratis

PythonMachine Learning4 jam13 videos44 Latihan3,200 XP65,715Bukti Prestasi

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Deskripsi Kursus

Dalam kursus ini Anda akan mempelajari cara menggunakan klasifikator linear, khususnya logistic regression dan support vector machines, dengan scikit-learn. Setelah mempelajari cara menerapkan metode-metode ini, Anda akan mendalami gagasan di baliknya dan memahami apa yang membuatnya bekerja. Di akhir kursus, Anda akan mengetahui cara melatih, menguji, dan menyetel klasifikator linear ini di Python. Anda juga akan memiliki landasan konseptual untuk memahami banyak algoritme Machine Learning lainnya.

Persyaratan

Supervised Learning with scikit-learn

1

Applying logistic regression and SVM

In this chapter you will learn the basics of applying logistic regression and support vector machines (SVMs) to classification problems. You'll use the scikit-learn library to fit classification models to real data.

scikit-learn refresher

KNN classification

Comparing models

Overfitting

Applying logistic regression and SVM

Running LogisticRegression and SVC

Sentiment analysis for movie reviews

Linear classifiers

Which decision boundary is linear?

Visualizing decision boundaries

2

Loss functions

In this chapter you will discover the conceptual framework behind logistic regression and SVMs. This will let you delve deeper into the inner workings of these models.

Linear classifiers: the coefficients

How models make predictions

Changing the model coefficients

What is a loss function?

The 0-1 loss

Minimizing a loss function

Loss function diagrams

Classification loss functions

Comparing the logistic and hinge losses

Implementing logistic regression

3

Logistic regression

In this chapter you will delve into the details of logistic regression. You'll learn all about regularization and how to interpret model output.

Logistic regression and regularization

Regularized logistic regression

Logistic regression and feature selection

Identifying the most positive and negative words

Logistic regression and probabilities

Getting class probabilities

Regularization and probabilities

Visualizing easy and difficult examples

Multi-class logistic regression

Counting the coefficients

Fitting multi-class logistic regression

Visualizing multi-class logistic regression

One-vs-rest SVM

4

Support Vector Machines

In this chapter you will learn all about the details of support vector machines. You'll learn about tuning hyperparameters for these models and using kernels to fit non-linear decision boundaries.

Support vectors

Support vector definition

Effect of removing examples

Kernel SVMs

GridSearchCV warm-up

Jointly tuning gamma and C with GridSearchCV

Comparing logistic regression and SVM (and beyond)

An advantage of SVMs

An advantage of logistic regression

Using SGDClassifier

Klasifikator Linear di Python

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