# Interview question — Predict on the Iris dataset using sklearn’s DecisionTreeClassifier

Because there is a great deal of competition in the data science field, it is a good idea study as many practice interview questions as possible in order to discover what skills interviewers want to see in a data science interview.

One interview question that I have found is with regard to the famous and easy to use iris dataset:-

Build a decision tree model on the iris dataset where the variable ‘variety’ is dependent and all other variables are independent. Find the accuracy of the model.

The Iris flower data set,or Fisher’s Iris data set, is a multivariate data set used and made famous by the British statistician and biologist Ronald Fisher in his 1936 paper, ‘The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis’. The data set consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimetres. Based on the combination of these four features, Fisher developed a linear discriminant model to distinguish the species from each other. Fisher’s paper was published in the Annals of Eugenics (today the Annals of Human Genetics) and includes discussion of…