Doing basic classifications with decision trees

Here, we perform basic classification with decision trees. Decision trees for classification are sequences of decisions that determine a classification, or a categorical outcome. Additionally, the decision tree can be examined in SQL by other individuals within the same company looking at the data.

Getting ready

Start by loading the iris dataset once again and dividing the data into training and testing sets:

from sklearn.datasets import load_iris

iris = load_iris()

X = iris.data
y = iris.target

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y)

How to do it...

1. Import the decision tree classifier and train it on the training set:

from sklearn.tree import DecisionTreeClassifier

dtc = DecisionTreeClassifier()     #Instantiate tree class
dtc.fit(X_train, y_train)

2. Then measure the accuracy on the test set:

from sklearn.metrics import accuracy_score

y_pred = dtc...