Random Forest Classification of the Breast Cancer Dataset

This notebook demonstrates how to use a Random Forest classifier to predict breast cancer diagnosis. The dataset contains 569 samples with 30 features computed from digitized images of breast mass cells.

Target Variable: - 0 = Malignant (cancerous) - 1 = Benign (non-cancerous)

Import Modules

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report, confusion_matrix
import pandas as pd

Load the Breast Cancer Dataset

# Load the dataset from sklearn datasets
bc = load_breast_cancer()

# Convert to Pandas data frame and display the first 10 rows
df = pd.DataFrame(data=bc.data, columns=bc.feature_names)
df["target"] = bc.target
df.head(10)

	mean radius	mean texture	mean perimeter	mean area	mean smoothness	mean compactness	mean concavity	mean concave points	mean symmetry	mean fractal dimension	...	worst texture	worst perimeter	worst area	worst smoothness	worst compactness	worst concavity	worst concave points	worst symmetry	worst fractal dimension
0	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.30010	0.14710	0.2419	0.07871	...	17.33	184.60	2019.0	0.1622	0.6656	0.7119	0.2654	0.4601	0.11890
1	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.08690	0.07017	0.1812	0.05667	...	23.41	158.80	1956.0	0.1238	0.1866	0.2416	0.1860	0.2750	0.08902
2	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.19740	0.12790	0.2069	0.05999	...	25.53	152.50	1709.0	0.1444	0.4245	0.4504	0.2430	0.3613	0.08758
3	11.42	20.38	77.58	386.1	0.14250	0.28390	0.24140	0.10520	0.2597	0.09744	...	26.50	98.87	567.7	0.2098	0.8663	0.6869	0.2575	0.6638	0.17300
4	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.19800	0.10430	0.1809	0.05883	...	16.67	152.20	1575.0	0.1374	0.2050	0.4000	0.1625	0.2364	0.07678
5	12.45	15.70	82.57	477.1	0.12780	0.17000	0.15780	0.08089	0.2087	0.07613	...	23.75	103.40	741.6	0.1791	0.5249	0.5355	0.1741	0.3985	0.12440
6	18.25	19.98	119.60	1040.0	0.09463	0.10900	0.11270	0.07400	0.1794	0.05742	...	27.66	153.20	1606.0	0.1442	0.2576	0.3784	0.1932	0.3063	0.08368
7	13.71	20.83	90.20	577.9	0.11890	0.16450	0.09366	0.05985	0.2196	0.07451	...	28.14	110.60	897.0	0.1654	0.3682	0.2678	0.1556	0.3196	0.11510
8	13.00	21.82	87.50	519.8	0.12730	0.19320	0.18590	0.09353	0.2350	0.07389	...	30.73	106.20	739.3	0.1703	0.5401	0.5390	0.2060	0.4378	0.10720
9	12.46	24.04	83.97	475.9	0.11860	0.23960	0.22730	0.08543	0.2030	0.08243	...	40.68	97.65	711.4	0.1853	1.0580	1.1050	0.2210	0.4366	0.20750

10 rows × 31 columns

# Print data shape (569 samples, 31 features)
df.shape

(569, 31)

# Show the features and other basic information
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 569 entries, 0 to 568
Data columns (total 31 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   mean radius              569 non-null    float64
 1   mean texture             569 non-null    float64
 2   mean perimeter           569 non-null    float64
 3   mean area                569 non-null    float64
 4   mean smoothness          569 non-null    float64
 5   mean compactness         569 non-null    float64
 6   mean concavity           569 non-null    float64
 7   mean concave points      569 non-null    float64
 8   mean symmetry            569 non-null    float64
 9   mean fractal dimension   569 non-null    float64
 10  radius error             569 non-null    float64
 11  texture error            569 non-null    float64
 12  perimeter error          569 non-null    float64
 13  area error               569 non-null    float64
 14  smoothness error         569 non-null    float64
 15  compactness error        569 non-null    float64
 16  concavity error          569 non-null    float64
 17  concave points error     569 non-null    float64
 18  symmetry error           569 non-null    float64
 19  fractal dimension error  569 non-null    float64
 20  worst radius             569 non-null    float64
 21  worst texture            569 non-null    float64
 22  worst perimeter          569 non-null    float64
 23  worst area               569 non-null    float64
 24  worst smoothness         569 non-null    float64
 25  worst compactness        569 non-null    float64
 26  worst concavity          569 non-null    float64
 27  worst concave points     569 non-null    float64
 28  worst symmetry           569 non-null    float64
 29  worst fractal dimension  569 non-null    float64
 30  target                   569 non-null    int64  
dtypes: float64(30), int64(1)
memory usage: 137.9 KB

# Check the distribution of target values
df['target'].value_counts()
# There are 357 patients with malignant tumors (label 1), and 212 patients with benign tumors (label 0)

	count
target
1	357
0	212

dtype: int64

Prepare Training and Testing Datasets

# Define features (X) and targets (y) variables
X = df.drop('target', axis=1)
y = df['target']

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=123, stratify=y)

# Print the shapes of data and label sets
print('Training data features', X_train.shape)
print('Training labels', y_train.shape)
print('Testing data features', X_test.shape)
print('Testing labels', y_test.shape)

Training data features (398, 30)
Training labels (398,)
Testing data features (171, 30)
Testing labels (171,)

Train a Random Forest Model

# Create an instance of a Random Forest model
model = RandomForestClassifier(random_state=42)

# Fit the model to the training dataset
model.fit(X_train, y_train)

RandomForestClassifier(random_state=42)

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Evaluate the Model

# Predict on test dataset
model_predictions = model.predict(X_test)

# Calculate and print the accuracy
accuracy = accuracy_score(y_test, model_predictions)
print(f"Accuracy on test dataset: {accuracy*100:5.2f}%")

Accuracy on test dataset: 97.66%

# Print confusion matrix
print("Confusion Matrix:")
print(confusion_matrix(y_test, model_predictions))

Confusion Matrix:
[[ 63   1]
 [  3 104]]

The confusion matrix shows: - True Negatives (top-left): Correctly predicted benign cases - False Positives (top-right): Benign cases incorrectly predicted as malignant - False Negatives (bottom-left): Malignant cases incorrectly predicted as benign - True Positives (bottom-right): Correctly predicted malignant cases

# Print classification report
print("Classification report:")
print(classification_report(y_test, model_predictions))

Classification report:
              precision    recall  f1-score   support

           0       0.95      0.98      0.97        64
           1       0.99      0.97      0.98       107

    accuracy                           0.98       171
   macro avg       0.97      0.98      0.98       171
weighted avg       0.98      0.98      0.98       171

The report provides detailed metrics: - Precision: Of all predicted positives, how many were correct? - Recall: Of all actual positives, how many were detected? - F1-Score: Harmonic mean of precision and recall.