Decision Trees
Exploring Tree-Based Machine Learning Algorithms
Program outputDecision Trees
This README file provides information on Decision Trees and their implementation. Decision Trees are supervised machine learning algorithms used for classification and regression tasks. They partition the feature space into segments based on feature values and make predictions by following the learned decision rules. Below is a breakdown of the content covered in this README.
Table of Contents
- Part A: Decision Trees
- Part B: Exploratory Data Analysis (EDA)
- Part C: Label Encoding
- Part D: Decision Tree Classifier
- Part E: Comparison with Another Dataset
- Conclusion
Part A: Decision Trees
This section introduces Decision Trees and their role in machine learning. It also mentions the dataset used for the subsequent analysis.
Part B: Exploratory Data Analysis (EDA)
The EDA section focuses on exploring the dataset. It may include tasks such as data visualization, understanding the distribution of features, identifying missing values, etc.
Part C: Label Encoding
In this section, categorical features are encoded into numerical values to prepare the dataset for training a Decision Tree classifier.
Part D: Decision Tree Classifier
The DecisionTreeClassifier from the sklearn.tree module is used to build a Decision Tree model. Two different criteria, “gini” and “entropy,” are applied to compare the performance. The decision trees are visualized using the tree.plot_tree function.
Part E: Comparison with Another Dataset
Another dataset, “diabetes_1,” is loaded and subjected to the same operations as described in the previous sections. The performance and accuracy of the Decision Tree classifier with different criteria are compared.
Conclusion
The conclusion section provides a summary of the results obtained from the analysis. Based on the results, it states which criterion (Gini or Entropy) performs better for the given dataset.
This README provides a comprehensive overview of Decision Trees, their implementation, and the analysis performed on the provided dataset. Follow the sections in order to understand the concepts, explore the data, and evaluate the performance of the Decision Tree classifier.
# import libraries
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
Decision Trees:
Loading the dataset:
df = pd.read_csv("/content/Decision_trees_1.csv")
EDA:
df.head()
| Customer ID | Gender | Car Type | Shirt Size | Class | |
|---|---|---|---|---|---|
| 0 | 1 | M | Family | Small | C0 |
| 1 | 2 | M | Sports | Medium | C0 |
| 2 | 3 | M | Sports | Medium | C0 |
| 3 | 4 | M | Sports | Large | C0 |
| 4 | 5 | M | Sports | Extra large | C0 |
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buttonEl.style.display =
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async function convertToInteractive(key) {
const element = document.querySelector('#df-77eac7b7-2bd8-454b-a42b-c7544453ffda');
const dataTable =
await google.colab.kernel.invokeFunction('convertToInteractive',
[key], {});
if (!dataTable) return;
const docLinkHtml = 'Like what you see? Visit the ' +
'<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
+ ' to learn more about interactive tables.';
element.innerHTML = '';
dataTable['output_type'] = 'display_data';
await google.colab.output.renderOutput(dataTable, element);
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df.describe()
| Customer ID | |
|---|---|
| count | 20.00000 |
| mean | 10.50000 |
| std | 5.91608 |
| min | 1.00000 |
| 25% | 5.75000 |
| 50% | 10.50000 |
| 75% | 15.25000 |
| max | 20.00000 |
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const buttonEl =
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buttonEl.style.display =
google.colab.kernel.accessAllowed ? 'block' : 'none';
async function convertToInteractive(key) {
const element = document.querySelector('#df-1cc6f6c9-5a32-4416-8385-ad4ae25ac6c5');
const dataTable =
await google.colab.kernel.invokeFunction('convertToInteractive',
[key], {});
if (!dataTable) return;
const docLinkHtml = 'Like what you see? Visit the ' +
'<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
+ ' to learn more about interactive tables.';
element.innerHTML = '';
dataTable['output_type'] = 'display_data';
await google.colab.output.renderOutput(dataTable, element);
const docLink = document.createElement('div');
docLink.innerHTML = docLinkHtml;
element.appendChild(docLink);
}
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df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20 entries, 0 to 19
Data columns (total 5 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Customer ID 20 non-null int64
1 Gender 20 non-null object
2 Car Type 20 non-null object
3 Shirt Size 20 non-null object
4 Class 20 non-null object
dtypes: int64(1), object(4)
memory usage: 928.0+ bytes
df.shape
(20, 5)
from sklearn import preprocessing
Label Encoding the features (Categorical -> Numerical data).
label_encoder = preprocessing.LabelEncoder()
label_encoder = preprocessing.LabelEncoder()
df["Class"] = label_encoder.fit_transform(df["Class"])
df["Gender"] = label_encoder.fit_transform(df["Gender"])
df["Car Type"] = label_encoder.fit_transform(df["Car Type"])
df["Shirt Size"] = label_encoder.fit_transform(df["Shirt Size"])
df
| Customer ID | Gender | Car Type | Shirt Size | Class | |
|---|---|---|---|---|---|
| 0 | 1 | 1 | 0 | 3 | 0 |
| 1 | 2 | 1 | 2 | 2 | 0 |
| 2 | 3 | 1 | 2 | 2 | 0 |
| 3 | 4 | 1 | 2 | 1 | 0 |
| 4 | 5 | 1 | 2 | 0 | 0 |
| 5 | 6 | 1 | 2 | 0 | 0 |
| 6 | 7 | 0 | 2 | 3 | 0 |
| 7 | 8 | 0 | 2 | 3 | 0 |
| 8 | 9 | 0 | 2 | 2 | 0 |
| 9 | 10 | 0 | 1 | 1 | 0 |
| 10 | 11 | 1 | 0 | 1 | 1 |
| 11 | 12 | 1 | 0 | 0 | 1 |
| 12 | 13 | 1 | 0 | 2 | 1 |
| 13 | 14 | 1 | 1 | 0 | 1 |
| 14 | 15 | 0 | 1 | 3 | 1 |
| 15 | 16 | 0 | 1 | 3 | 1 |
| 16 | 17 | 0 | 1 | 2 | 1 |
| 17 | 18 | 0 | 1 | 2 | 1 |
| 18 | 19 | 0 | 1 | 2 | 1 |
| 19 | 20 | 0 | 1 | 1 | 1 |
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document.querySelector('#df-329b65dd-b1e2-4917-abe5-88103383ca92 button.colab-df-convert');
buttonEl.style.display =
google.colab.kernel.accessAllowed ? 'block' : 'none';
async function convertToInteractive(key) {
const element = document.querySelector('#df-329b65dd-b1e2-4917-abe5-88103383ca92');
const dataTable =
await google.colab.kernel.invokeFunction('convertToInteractive',
[key], {});
if (!dataTable) return;
const docLinkHtml = 'Like what you see? Visit the ' +
'<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
+ ' to learn more about interactive tables.';
element.innerHTML = '';
dataTable['output_type'] = 'display_data';
await google.colab.output.renderOutput(dataTable, element);
const docLink = document.createElement('div');
docLink.innerHTML = docLinkHtml;
element.appendChild(docLink);
}
</script>
</div>
X = df[["Gender", "Car Type", "Shirt Size"]]
X.head()
| Gender | Car Type | Shirt Size | |
|---|---|---|---|
| 0 | 1 | 0 | 3 |
| 1 | 1 | 2 | 2 |
| 2 | 1 | 2 | 2 |
| 3 | 1 | 2 | 1 |
| 4 | 1 | 2 | 0 |
<script>
const buttonEl =
document.querySelector('#df-ab5e066a-86ff-4f83-81d4-d988bce77f1d button.colab-df-convert');
buttonEl.style.display =
google.colab.kernel.accessAllowed ? 'block' : 'none';
async function convertToInteractive(key) {
const element = document.querySelector('#df-ab5e066a-86ff-4f83-81d4-d988bce77f1d');
const dataTable =
await google.colab.kernel.invokeFunction('convertToInteractive',
[key], {});
if (!dataTable) return;
const docLinkHtml = 'Like what you see? Visit the ' +
'<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
+ ' to learn more about interactive tables.';
element.innerHTML = '';
dataTable['output_type'] = 'display_data';
await google.colab.output.renderOutput(dataTable, element);
const docLink = document.createElement('div');
docLink.innerHTML = docLinkHtml;
element.appendChild(docLink);
}
</script>
</div>
Y = df["Class"]
Y.head()
0 0
1 0
2 0
3 0
4 0
Name: Class, dtype: int64
from sklearn.tree import DecisionTreeClassifier
clf_gini = DecisionTreeClassifier(criterion = "gini", random_state = 100, max_depth = None, min_samples_leaf = 1)
clf_gini.fit(X, Y)
DecisionTreeClassifier(random_state=100)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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DecisionTreeClassifier(random_state=100)
from sklearn import tree
Car Type seems to be the Root Node from the given image:
tree.plot_tree(clf_gini)
[Text(0.6666666666666666, 0.9, 'x[1] <= 1.5\ngini = 0.5\nsamples = 20\nvalue = [10, 10]'),
Text(0.5555555555555556, 0.7, 'x[2] <= 2.5\ngini = 0.278\nsamples = 12\nvalue = [2, 10]'),
Text(0.3333333333333333, 0.5, 'x[2] <= 1.5\ngini = 0.198\nsamples = 9\nvalue = [1, 8]'),
Text(0.2222222222222222, 0.3, 'x[0] <= 0.5\ngini = 0.32\nsamples = 5\nvalue = [1, 4]'),
Text(0.1111111111111111, 0.1, 'gini = 0.5\nsamples = 2\nvalue = [1, 1]'),
Text(0.3333333333333333, 0.1, 'gini = 0.0\nsamples = 3\nvalue = [0, 3]'),
Text(0.4444444444444444, 0.3, 'gini = 0.0\nsamples = 4\nvalue = [0, 4]'),
Text(0.7777777777777778, 0.5, 'x[0] <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]'),
Text(0.6666666666666666, 0.3, 'gini = 0.0\nsamples = 2\nvalue = [0, 2]'),
Text(0.8888888888888888, 0.3, 'gini = 0.0\nsamples = 1\nvalue = [1, 0]'),
Text(0.7777777777777778, 0.7, 'gini = 0.0\nsamples = 8\nvalue = [8, 0]')]
Using Entropy now, instead of Gini Index:
clf_entropy = DecisionTreeClassifier(criterion = "entropy", random_state = 100, max_depth = None, min_samples_leaf = 1)
clf_entropy.fit(X, Y)
DecisionTreeClassifier(criterion='entropy', random_state=100)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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DecisionTreeClassifier(criterion='entropy', random_state=100)
tree.plot_tree(clf_entropy)
[Text(0.5, 0.9, 'x[1] <= 1.5\nentropy = 1.0\nsamples = 20\nvalue = [10, 10]'),
Text(0.375, 0.7, 'x[2] <= 0.5\nentropy = 0.65\nsamples = 12\nvalue = [2, 10]'),
Text(0.25, 0.5, 'entropy = 0.0\nsamples = 2\nvalue = [0, 2]'),
Text(0.5, 0.5, 'x[1] <= 0.5\nentropy = 0.722\nsamples = 10\nvalue = [2, 8]'),
Text(0.25, 0.3, 'x[2] <= 2.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]'),
Text(0.125, 0.1, 'entropy = 0.0\nsamples = 2\nvalue = [0, 2]'),
Text(0.375, 0.1, 'entropy = 0.0\nsamples = 1\nvalue = [1, 0]'),
Text(0.75, 0.3, 'x[2] <= 1.5\nentropy = 0.592\nsamples = 7\nvalue = [1, 6]'),
Text(0.625, 0.1, 'entropy = 1.0\nsamples = 2\nvalue = [1, 1]'),
Text(0.875, 0.1, 'entropy = 0.0\nsamples = 5\nvalue = [0, 5]'),
Text(0.625, 0.7, 'entropy = 0.0\nsamples = 8\nvalue = [8, 0]')]
Loading another dataset:
df = pd.read_csv("/content/diabetes_1.csv")
EDA:
df.head()
| Pregnancies | Glucose | BloodPressure | SkinThickness | Insulin | BMI | DiabetesPedigreeFunction | Age | Outcome | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 6 | 148 | 72 | 35 | 0 | 33.6 | 0.627 | 50 | 1 |
| 1 | 1 | 85 | 66 | 29 | 0 | 26.6 | 0.351 | 31 | 0 |
| 2 | 8 | 183 | 64 | 0 | 0 | 23.3 | 0.672 | 32 | 1 |
| 3 | 1 | 89 | 66 | 23 | 94 | 28.1 | 0.167 | 21 | 0 |
| 4 | 0 | 137 | 40 | 35 | 168 | 43.1 | 2.288 | 33 | 1 |
<script>
const buttonEl =
document.querySelector('#df-7922f685-4940-4cf2-aac0-d0850093d20e button.colab-df-convert');
buttonEl.style.display =
google.colab.kernel.accessAllowed ? 'block' : 'none';
async function convertToInteractive(key) {
const element = document.querySelector('#df-7922f685-4940-4cf2-aac0-d0850093d20e');
const dataTable =
await google.colab.kernel.invokeFunction('convertToInteractive',
[key], {});
if (!dataTable) return;
const docLinkHtml = 'Like what you see? Visit the ' +
'<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
+ ' to learn more about interactive tables.';
element.innerHTML = '';
dataTable['output_type'] = 'display_data';
await google.colab.output.renderOutput(dataTable, element);
const docLink = document.createElement('div');
docLink.innerHTML = docLinkHtml;
element.appendChild(docLink);
}
</script>
</div>
df.describe()
| Pregnancies | Glucose | BloodPressure | SkinThickness | Insulin | BMI | DiabetesPedigreeFunction | Age | Outcome | |
|---|---|---|---|---|---|---|---|---|---|
| count | 768.000000 | 768.000000 | 768.000000 | 768.000000 | 768.000000 | 768.000000 | 768.000000 | 768.000000 | 768.000000 |
| mean | 3.845052 | 120.894531 | 69.105469 | 20.536458 | 79.799479 | 31.992578 | 0.471876 | 33.240885 | 0.348958 |
| std | 3.369578 | 31.972618 | 19.355807 | 15.952218 | 115.244002 | 7.884160 | 0.331329 | 11.760232 | 0.476951 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.078000 | 21.000000 | 0.000000 |
| 25% | 1.000000 | 99.000000 | 62.000000 | 0.000000 | 0.000000 | 27.300000 | 0.243750 | 24.000000 | 0.000000 |
| 50% | 3.000000 | 117.000000 | 72.000000 | 23.000000 | 30.500000 | 32.000000 | 0.372500 | 29.000000 | 0.000000 |
| 75% | 6.000000 | 140.250000 | 80.000000 | 32.000000 | 127.250000 | 36.600000 | 0.626250 | 41.000000 | 1.000000 |
| max | 17.000000 | 199.000000 | 122.000000 | 99.000000 | 846.000000 | 67.100000 | 2.420000 | 81.000000 | 1.000000 |
<script>
const buttonEl =
document.querySelector('#df-1101316d-079b-4cd5-a160-5eb413ae4ad7 button.colab-df-convert');
buttonEl.style.display =
google.colab.kernel.accessAllowed ? 'block' : 'none';
async function convertToInteractive(key) {
const element = document.querySelector('#df-1101316d-079b-4cd5-a160-5eb413ae4ad7');
const dataTable =
await google.colab.kernel.invokeFunction('convertToInteractive',
[key], {});
if (!dataTable) return;
const docLinkHtml = 'Like what you see? Visit the ' +
'<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
+ ' to learn more about interactive tables.';
element.innerHTML = '';
dataTable['output_type'] = 'display_data';
await google.colab.output.renderOutput(dataTable, element);
const docLink = document.createElement('div');
docLink.innerHTML = docLinkHtml;
element.appendChild(docLink);
}
</script>
</div>
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 768 entries, 0 to 767
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Pregnancies 768 non-null int64
1 Glucose 768 non-null int64
2 BloodPressure 768 non-null int64
3 SkinThickness 768 non-null int64
4 Insulin 768 non-null int64
5 BMI 768 non-null float64
6 DiabetesPedigreeFunction 768 non-null float64
7 Age 768 non-null int64
8 Outcome 768 non-null int64
dtypes: float64(2), int64(7)
memory usage: 54.1 KB
df.shape
(768, 9)
Since this is a classification dataset (specifically binary), we don’t need the Outcomes attribute so we can drop that column from the dataset.
X1 = df.drop("Outcome", axis = 1)
X1.head()
| Pregnancies | Glucose | BloodPressure | SkinThickness | Insulin | BMI | DiabetesPedigreeFunction | Age | |
|---|---|---|---|---|---|---|---|---|
| 0 | 6 | 148 | 72 | 35 | 0 | 33.6 | 0.627 | 50 |
| 1 | 1 | 85 | 66 | 29 | 0 | 26.6 | 0.351 | 31 |
| 2 | 8 | 183 | 64 | 0 | 0 | 23.3 | 0.672 | 32 |
| 3 | 1 | 89 | 66 | 23 | 94 | 28.1 | 0.167 | 21 |
| 4 | 0 | 137 | 40 | 35 | 168 | 43.1 | 2.288 | 33 |
<script>
const buttonEl =
document.querySelector('#df-d4dfc623-2421-4dcf-b2e0-a3f323ef4ef7 button.colab-df-convert');
buttonEl.style.display =
google.colab.kernel.accessAllowed ? 'block' : 'none';
async function convertToInteractive(key) {
const element = document.querySelector('#df-d4dfc623-2421-4dcf-b2e0-a3f323ef4ef7');
const dataTable =
await google.colab.kernel.invokeFunction('convertToInteractive',
[key], {});
if (!dataTable) return;
const docLinkHtml = 'Like what you see? Visit the ' +
'<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
+ ' to learn more about interactive tables.';
element.innerHTML = '';
dataTable['output_type'] = 'display_data';
await google.colab.output.renderOutput(dataTable, element);
const docLink = document.createElement('div');
docLink.innerHTML = docLinkHtml;
element.appendChild(docLink);
}
</script>
</div>
y1 = df["Outcome"]
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X1, y1, test_size = 0.2, random_state = 0) # 80:20 ratio for train to test.
Testing with Gini Index first:
model_gini = DecisionTreeClassifier(criterion = "gini", random_state = 100, max_depth = None, min_samples_leaf = 1)
model_gini.fit(X_train, Y_train)
DecisionTreeClassifier(random_state=100)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeClassifier(random_state=100)
tree.plot_tree(model_gini)
[Text(0.4033954326923077, 0.9642857142857143, 'x[1] <= 123.5\ngini = 0.461\nsamples = 614\nvalue = [393, 221]'),
Text(0.14483173076923078, 0.8928571428571429, 'x[7] <= 28.5\ngini = 0.301\nsamples = 352\nvalue = [287, 65]'),
Text(0.052884615384615384, 0.8214285714285714, 'x[5] <= 30.95\ngini = 0.162\nsamples = 202\nvalue = [184, 18]'),
Text(0.028846153846153848, 0.75, 'x[0] <= 7.0\ngini = 0.036\nsamples = 110\nvalue = [108, 2]'),
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Text(0.019230769230769232, 0.5357142857142857, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
Text(0.038461538461538464, 0.5357142857142857, 'gini = 0.0\nsamples = 9\nvalue = [9, 0]'),
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Text(0.07692307692307693, 0.75, 'x[2] <= 53.0\ngini = 0.287\nsamples = 92\nvalue = [76, 16]'),
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Text(0.0673076923076923, 0.5357142857142857, 'x[5] <= 45.35\ngini = 0.135\nsamples = 55\nvalue = [51, 4]'),
Text(0.057692307692307696, 0.4642857142857143, 'x[4] <= 36.5\ngini = 0.105\nsamples = 54\nvalue = [51, 3]'),
Text(0.04807692307692308, 0.39285714285714285, 'x[2] <= 82.5\ngini = 0.266\nsamples = 19\nvalue = [16, 3]'),
Text(0.038461538461538464, 0.32142857142857145, 'x[1] <= 111.5\ngini = 0.198\nsamples = 18\nvalue = [16, 2]'),
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Text(0.07692307692307693, 0.4642857142857143, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
Text(0.10576923076923077, 0.5357142857142857, 'x[2] <= 69.0\ngini = 0.358\nsamples = 30\nvalue = [23, 7]'),
Text(0.09615384615384616, 0.4642857142857143, 'x[1] <= 88.5\ngini = 0.492\nsamples = 16\nvalue = [9, 7]'),
Text(0.08653846153846154, 0.39285714285714285, 'gini = 0.0\nsamples = 7\nvalue = [7, 0]'),
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y_pred1 = model_gini.predict(X_test)
from sklearn import metrics
print("Accuracy Score : ", metrics.accuracy_score(Y_test, y_pred1))
Accuracy Score : 0.8051948051948052
Checking for a max depth value of 13.
model_gini1 = DecisionTreeClassifier(criterion = "gini", random_state = 100, max_depth = 13, min_samples_leaf = 1)
model_gini1.fit(X_train, Y_train)
DecisionTreeClassifier(max_depth=13, random_state=100)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeClassifier(max_depth=13, random_state=100)
tree.plot_tree(model_gini1)
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y_pred2 = model_gini1.predict(X_test)
print("Accuracy Score : ", metrics.accuracy_score(Y_test, y_pred2))
Accuracy Score : 0.8051948051948052
Trying with Entropy now:
model_entropy = DecisionTreeClassifier(criterion = "entropy", random_state = 100, max_depth = None, min_samples_leaf = 1)
model_entropy.fit(X_train, Y_train)
DecisionTreeClassifier(criterion='entropy', random_state=100)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeClassifier(criterion='entropy', random_state=100)
tree.plot_tree(model_entropy)
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y_pred3 = model_entropy.predict(X_test)
print("Accuracy Score : ", metrics.accuracy_score(Y_test, y_pred3))
Accuracy Score : 0.7272727272727273
Hence from the above results, we see that for this dataset, the Gini performs better due to better accuracy results.
Srihari Thyagarajan
B Tech AI Senior Student
Hi, I’m Haleshot, a final-year student studying B Tech Artificial Intelligence. I like projects relating to ML, AI, DL, CV, NLP, Image Processing, etc. Currently exploring Python, FastAPI, projects involving AI and platforms such as HuggingFace and Kaggle.