Machine Learning on a Cancer Dataset - Part 6

In this 6th video of the series, we're actually training our first machine learning classifier, KNN, on the cancer dataset in scikit-learn.

The purpose is to predict whether an image is of a malignant or benign tumor.

The code:

from sklearn.datasets import load_breast_cancer
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split

import matplotlib.pyplot as plt
%matplotlib inline

cancer = load_breast_cancer()

X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, stratify=cancer.target, random_state=42)

knn = KNeighborsClassifier()
knn.fit(X_train, y_train)

print('Accuracy of KNN n-5, on the training set: {:.3f}'.format(knn.score(X_train, y_train)))
print('Accuracy of KNN n-5, on the test set: {:.3f}'.format(knn.score(X_test, y_test)))

And the output:

Accuracy of KNN n-5, on the training set: 0.946
Accuracy of KNN n-5, on the test set: 0.930

For a walkthrough, see the video below.

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As a reminder:

In this series I'm going to explore the cancer dataset that comes pre-loaded with scikit-learn. The purpose is to train the classifiers on this dataset, which consists of labeled data: ~569 tumor samples, each labeled malignant or benign, and then use them on new, unlabeled data.

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Previous videos in this series:

1. Machine Learning on a Cancer Dataset - Part 1
2. Machine Learning on a Cancer Dataset - Part 2
3. Machine Learning on a Cancer Dataset - Part 3
4. Machine Learning on a Cancer Dataset - Part 4
5. Machine Learning on a Cancer Dataset - Part 5

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To stay in touch with me, follow @cristi

#machine-learning #science #python

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Cristi Vlad, Self-Experimenter and Author