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.

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

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 vector

Definisi support vector

Dampak menghapus contoh

SVM Kernel

Pemanasan GridSearchCV

Menyetel gamma dan C secara bersamaan dengan GridSearchCV

Membandingkan regresi logistik dan SVM (dan seterusnya)

Satu keunggulan SVM

Keunggulan logistic regression

Menggunakan SGDClassifier

Kesimpulan

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

Satu keunggulan SVM

Klasifikator Linear di Python

Latihan interaktif praktis