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.

Logistische regressie en regularisatie

Geregulariseerde logistic regression

Logistic regression en featureselectie

De meest positieve en negatieve woorden identificeren

Logistische regressie en kansen

Kanskansen van klassen bepalen

Regularisatie en kansen

Makkelijke en moeilijke voorbeelden visualiseren

Logistische regressie voor meerdere klassen

Het aantal coëfficiënten tellen

Multi-class logistic regression fitten

Multiclass logistic regression visualiseren

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

In deze cursus leer je alles over het gebruik van lineaire classificatoren, specifiek logistic regression en support vector machines, met scikit-learn. Nadat je hebt geleerd hoe je deze methoden toepast, duik je in de achterliggende ideeën en ontdek je wat ze echt laat werken. Aan het einde van deze cursus weet je hoe je deze lineaire classificatoren in Python traint, test en afstemt. Je hebt dan ook een conceptuele basis om veel andere Machine Learning-algoritmen te begrijpen.

Supervised Learning with scikit-learn

In deze cursus leer je over lineaire classificatoren zoals logistische regressie en SVM.

Lineaire classificatoren in Python

In deze cursus leer je de details van lineaire classifiers zoals logistische regressie en SVM.

Wetenschapper op het gebied van machine learning in Python

Begeleid machine learning in Python

Kanskansen van klassen bepalen

Lineaire classificatoren in Python

Praktische interactieve oefening