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

Ulasan singkat scikit-learn

Klasifikasi KNN

Membandingkan model

Overfitting

Menerapkan logistic regression dan SVM

Menjalankan LogisticRegression dan SVC

Analisis sentimen untuk ulasan film

Klasifikator linear

Batas keputusan mana yang bersifat linear?

Memvisualisasikan batas keputusan

Applying logistic regression and SVM

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

Loss functions

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

Logistic regression

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

Conclusion

Support Vector Machines

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.

Supervised Learning with scikit-learn

Dalam kursus ini, Anda akan mempelajari rincian pengklasifikasi linier seperti regresi logistik dan SVM.

Klasifikator Linear di Python

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

Ilmuwan Pembelajaran Mesin dalam Python

Pembelajaran Mesin Terawasi dalam Python

Batas keputusan mana yang bersifat linear?

Klasifikator Linear di Python

Latihan interaktif praktis