Import Data

import pandas as pd
from sklearn.datasets import load_iris
from sklearn.datasets import load_diabetes
from sklearn.datasets import load_digits
from IPython.display import display
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = 'all'

Import Iris

iris = load_iris()
# type(iris)
iris.keys()

dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])

# iris.DESCR

X = pd.DataFrame(iris.data, columns=iris.feature_names)
y = pd.DataFrame(iris.target, columns=["class"]) # 0 - Setosa, 1 - versicolor, 2 - virginica

X.head()
y.head()

	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)
0	5.1	3.5	1.4	0.2
1	4.9	3.0	1.4	0.2
2	4.7	3.2	1.3	0.2
3	4.6	3.1	1.5	0.2
4	5.0	3.6	1.4	0.2

	class
0	0
1	0
2	0
3	0
4	0

Split Dataset

# Split data into train and test sets
from sklearn.model_selection import train_test_split

# Test size: %30 - compute train test split on "scaled" data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=1)

print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)

(105, 4) (45, 4) (105, 1) (45, 1)

# Display the test data
display(X_test.head())

# Display the shape of the variable y_test, which contains the labels of test data
display(y_test.shape)

# Display the labels of test data
display(y_test.head())

print(f"The type of the y_test: {type(y_test)}")
print(f"The type of the X_test: {type(X_test)}")

	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)
14	5.8	4.0	1.2	0.2
98	5.1	2.5	3.0	1.1
75	6.6	3.0	4.4	1.4
16	5.4	3.9	1.3	0.4
131	7.9	3.8	6.4	2.0

(45, 1)

	class
14	0
98	1
75	1
16	0
131	2

The type of the y_test: <class 'pandas.core.frame.DataFrame'>
The type of the X_test: <class 'pandas.core.frame.DataFrame'>

Decision Tree

from sklearn.tree import DecisionTreeClassifier

model = DecisionTreeClassifier(random_state=5) 

# Train the model, using the "train" data labels
model.fit(X_train, y_train)

# Predict labels using "test" data
y_test_predicted = model.predict(X_test)

DecisionTreeClassifier(random_state=5)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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# Plot the results into a canvas

import matplotlib.pyplot as plt
from sklearn.tree import plot_tree

plt.figure(figsize=(20,12))
modeltree = plot_tree(model,
                      feature_names=X.columns,
                      filled=True,
                      rounded=True,
                      fontsize=14
                      )

<Figure size 2000x1200 with 0 Axes>

# Print the distinct labels (in the Iris data, there are only 3 distinct labels)
print(f"Distinct labels in the Iris dataset: {iris.target_names}\n")

print(f"Predicted labels:\t {list(y_test_predicted)}")
print(f"Actual labels:\t\t {list(y_test)}")

Distinct labels in the Iris dataset: ['setosa' 'versicolor' 'virginica']

Predicted labels:	 [0, 1, 1, 0, 2, 1, 2, 0, 0, 2, 1, 0, 2, 1, 1, 0, 1, 1, 0, 0, 1, 1, 2, 0, 2, 1, 0, 0, 1, 2, 1, 2, 1, 2, 2, 0, 1, 0, 1, 2, 2, 0, 1, 2, 1]
Actual labels:		 ['class']

Model Assessment

from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score

confusionMatrix = confusion_matrix(y_test, y_test_predicted)

# Rows are the actual values, columns are predicted values
# Setosa Versicolor Virginica
# array([[14, 0,  0],
#        [ 0,  17, 1],
#        [ 0,  0, 12]])
# 1st row, 19 flowers are actually Setosa, and it is perfectly predicted as Setosa
# 2nd row, 13 flowers are correctly labeled as Versicolor out of the total 14. So, 1 of the flowers has been incorrectly classified 
# 3rd row, all 12 flowers have been correctly classified as Virginica


display(confusionMatrix)

array([[14,  0,  0],
       [ 0, 17,  1],
       [ 0,  1, 12]], dtype=int64)

from sklearn import metrics
import matplotlib.pyplot as plt

disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, y_test_predicted)
disp.figure_.suptitle("Confusion Matrix")
print(f"Confusion matrix:\n{disp.confusion_matrix}")

plt.show()

Text(0.5, 0.98, 'Confusion Matrix')

Confusion matrix:
[[14  0  0]
 [ 0 17  1]
 [ 0  1 12]]

accuracy_score(y_test, y_test_predicted)

0.9555555555555556

print(classification_report(y_test, y_test_predicted))

              precision    recall  f1-score   support

           0       1.00      1.00      1.00        14
           1       0.94      0.94      0.94        18
           2       0.92      0.92      0.92        13

    accuracy                           0.96        45
   macro avg       0.96      0.96      0.96        45
weighted avg       0.96      0.96      0.96        45