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.

Regressione logistica e regolarizzazione

Regressione logistica con regolarizzazione

Regressione logistica e selezione delle feature

Identificare le parole più positive e più negative

Regressione logistica e probabilità

Ottenere le probabilità di classe

Regolarizzazione e probabilità

Visualizzare esempi facili e difficili

Regressione logistica multiclasse

Contare i coefficienti

Adattare la regressione logistica multiclasse

Visualizzare la regressione logistica multiclasse

SVM one-vs-rest

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

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

Regolarizzazione e probabilità

Classificatori lineari in Python

Esercizio pratico interattivo