Exploring Decision Trees¶

1. Problem Definition¶

Iris dataset is the Hello World for Data Science, so we will practice a ML algorithm on this famous dataset.

Iris dataset contains five columns such as Petal Length, Petal Width, Sepal Length, Sepal Width and Species Type. Iris is a flowering plant, the researchers have measured various features of the different iris flowers and recorded digitally.

2. Importing Libraries¶

In [11]:
import pandas as pd
import matplotlib.pyplot as plt
import sklearn


In [12]:
df = pd.read_csv('https://raw.githubusercontent.com/arditoibryan/pythonkai/main/ML_for_beginners/Project4_decision_tree_classifier/iris_dataset.csv')
df

Out[12]:
label sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)
0 setosa 5.1 3.5 1.4 0.2
1 setosa 4.9 3.0 1.4 0.2
2 setosa 4.7 3.2 1.3 0.2
3 setosa 4.6 3.1 1.5 0.2
4 setosa 5.0 3.6 1.4 0.2
... ... ... ... ... ...
145 virginica 6.7 3.0 5.2 2.3
146 virginica 6.3 2.5 5.0 1.9
147 virginica 6.5 3.0 5.2 2.0
148 virginica 6.2 3.4 5.4 2.3
149 virginica 5.9 3.0 5.1 1.8

150 rows Ã— 5 columns

4. Cleaning the data¶

The dataset is perfect already, we do not need to preprocess it.

Why is the iris dataset perfect for classification?

Because all the features are very distinct from each other, and they do not overlap, so when the classification algorithm is trained, it wonâ€™t be unclear to assign some values to one or more labels.

The parallel coordinates graph can allow us to visualize every sample of the dataset by using parallel values on lines.

Check out the following code to visualize it.

Don't forget to install plotly with 'pip install plotly' in the terminal.

In [19]:
#Parallel coordinates graph of the iris dataset features

import plotly.express as px

df_iris = px.data.iris()
fig = px.parallel_coordinates(df_iris, color="species_id", labels={"species_id": "Species",
"sepal_width": "Sepal Width", "sepal_length": "Sepal Length",
"petal_width": "Petal Width", "petal_length": "Petal Length", },
color_continuous_scale=px.colors.diverging.Tealrose, color_continuous_midpoint=2)
fig.show()


The features belonging to each label are so different that we can even make a distinction with the human eye. If, for example, we had a new flower with a petal width of 2.5, we can immediately see from the graph that it belongs to species 3.

4. Separate label from features¶

In [13]:
X = df[list(df.columns[1:5])]
y = df[['label']]

In [14]:
X.head()

Out[14]:
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
In [15]:
y.head()

Out[15]:
label
0 setosa
1 setosa
2 setosa
3 setosa
4 setosa

5. Split the data into train and test¶

To measure the accuracy of our classification algorithm we will need to split the dataset into a test and train sample.

In [18]:
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, test_size=0.3)


6. Train the model¶

In [20]:
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeClassifier

clf = DecisionTreeClassifier(random_state=0)
clf.fit(X_train, y_train)
clf.score(X_test, y_test)

Out[20]:
0.9777777777777777

Perfect! Our decision tree score has reached 97% accuracy! We used this dataset on purpose to confirm that data really makes the difference when creating a model.

How was the tree trained?

In [40]:
from sklearn import tree
from matplotlib import pyplot as plt

plt.figure(figsize=(10,8))
tree.plot_tree(clf)
plt.show()


7. Making predictions¶

We are going to compare the real (true) label from the ones we predicted to see if we find the error that made our accuracy be different from 100%.

We are only doing this visually because we have a small dataset.

In [29]:
#show predicted dataset
pd.concat([y_test.reset_index(drop=True), pd.DataFrame(clf.predict(X_test))], axis=1)

Out[29]:
label 0
0 virginica virginica
1 versicolor versicolor
2 setosa setosa
3 virginica virginica
4 setosa setosa
5 virginica virginica
6 setosa setosa
7 versicolor versicolor
8 versicolor versicolor
9 versicolor versicolor
10 virginica virginica
11 versicolor versicolor
12 versicolor versicolor
13 versicolor versicolor
14 versicolor versicolor
15 setosa setosa
16 versicolor versicolor
17 versicolor versicolor
18 setosa setosa
19 setosa setosa
20 virginica virginica
21 versicolor versicolor
22 setosa setosa
23 setosa setosa
24 virginica virginica
25 setosa setosa
26 setosa setosa
27 versicolor versicolor
28 versicolor versicolor
29 setosa setosa
30 virginica virginica
31 versicolor versicolor
32 setosa setosa
33 virginica virginica
34 virginica virginica
35 versicolor versicolor
36 setosa setosa
37 versicolor virginica
38 versicolor versicolor
39 versicolor versicolor
40 virginica virginica
41 setosa setosa
42 virginica virginica
43 setosa setosa
44 setosa setosa