Thank you for reading this post, leave a comment below if you have any question. A gist with the full code for this example can be found here. Prf <- performance(pr, measure = "tpr", x.measure = "fpr") As a rule of thumb, a model with good predictive ability should have an AUC closer to 1 (1 is ideal) than to 0.5. The ROC is a curve generated by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings while the AUC is the area under the ROC curve. However, keep in mind that this result is somewhat dependent on the manual split of the data that I made earlier, therefore if you wish for a more precise score, you would be better off running some kind of cross validation such as k-fold cross validation.Īs a last step, we are going to plot the ROC curve and calculate the AUC (area under the curve) which are typical performance measurements for a binary classifier. The 0.84 accuracy on the test set is quite a good result. Print(paste('Accuracy',1-misClasificError)) MisClasificError <- mean(fitted.results != test$Survived) Note that for some applications different thresholds could be a better option. Make sure that the parameter na.strings is equal to c("") so that each missing value is coded as a NA. As a first step we load the csv data using the read.csv() function. When working with a real dataset we need to take into account the fact that some data might be missing or corrupted, therefore we need to prepare the dataset for our analysis. As you can see, we are going to use both categorical and continuous variables. The dataset (training) is a collection of data about some of the passengers (889 to be precise), and the goal of the competition is to predict the survival (either 1 if the passenger survived or 0 if they did not) based on some features such as the class of service, the sex, the age etc. There are different versions of this datasets freely available online, however I suggest to use the one available at Kaggle, since it is almost ready to be used (in order to download it you need to sign up to Kaggle). In this post I am going to fit a binary logistic regression model and explain each step. The function to be called is glm() and the fitting process is not so different from the one used in linear regression. R makes it very easy to fit a logistic regression model. A typical example for instance, would be classifying films between “Entertaining”, “borderline” or “boring”. In this second case we call the model “multinomial logistic regression”. In this post we call the model “binomial logistic regression”, since the variable to predict is binary, however, logistic regression can also be used to predict a dependent variable which can assume more than 2 values. A classical example used in machine learning is email classification: given a set of attributes for each email such as number of words, links and pictures, the algorithm should decide whether the email is spam (1) or not (0). In the simplest case scenario y is binary meaning that it can assume either the value 1 or 0. The categorical variable y, in general, can assume different values. The predictors can be continuous, categorical or a mix of both. The typical use of this model is predicting y given a set of predictors x. Logistic regression is a method for fitting a regression curve, y = f(x), when y is a categorical variable.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |