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 vectors

Definizione di support vector

Effetto della rimozione degli esempi

SVM con kernel

Riscaldamento con GridSearchCV

Ottimizzare insieme gamma e C con GridSearchCV

Confronto tra regressione logistica e SVM (e oltre)

Un vantaggio delle SVM

Un vantaggio della regressione logistica

Uso di SGDClassifier

Conclusione

Support Vector Machines

In questo corso imparerai a usare i classificatori lineari, in particolare la regressione logistica e le support vector machine, con scikit-learn. Dopo aver visto come applicare questi metodi, entrerai nelle idee che li guidano e scoprirai cosa li fa funzionare davvero. Al termine del corso saprai come addestrare, testare e ottimizzare questi classificatori lineari in Python. Avrai anche una base concettuale per comprendere molti altri algoritmi di Machine Learning.

Supervised Learning with scikit-learn

In questo corso imparerai i dettagli dei classificatori lineari come la regressione logistica e SVM.

Classificatori lineari in Python

Scienziato specializzato in apprendimento automatico in Python

Apprendimento automatico supervisionato in Python

Un vantaggio delle SVM

Classificatori lineari in Python

Esercizio pratico interattivo