4.5 LAB: Using the DecisionTreeClassifier() on the iris data

Write a program that splits a dataset into training and test set, builds a classification tree, and outputs a confusion matrix. The program should do the following:

• load the iris.csv dataset
• create a dataframe, x, using the petal_length and sepal_length as features
• create a dataframe, y, using species
• split the data into training and test sets with 0.25 test size and random_state = 0
• standardize x_train and x_test
• initialize the decision tree with criterion = "gini", random_state = 100, max_depth=3, min_samples_leaf=5
• run the decision tree on x_test
• generate the confusion matrix

The output should be:

[[14 0 0]

[ 0 13 1]

[ 0 1 9]]

Coding Structure Hint

1. # loads the necessary libraries
2. import pandas as pd
3. from sklearn.model_selection import train_test_split
4. from sklearn.preprocessing import StandardScaler
5. from sklearn.tree import DecisionTreeClassifier
6. from sklearn import datasets
7. from sklearn import metrics
9. # load the iris dataset
10. iris = datasets.load_iris()
12. x = # subset the data containing petal length and sepal length
14. y = # subset the data containing the labels
17. x_train, x_test, y_train, y_test = # splits the data into training and test sets for both x and y, with random_state = 0
19. # standardize x_train and x_test
22. cart = # initialize and run the decision tree using the following:
23. # criterion = "gini", random_state = 100, max_depth=3, min_samples_leaf=5
24. # fit the x_train and y_train data
25. y_pred = # use the cart model to make predictions using x_test
28. conf = # give the confusion matrix using y_test and y_pred
30. print(conf)