Breast-Cancer-Classification - Using SKLearn breast cancer dataset which contains 569 examples and 32 features classifying has been made with 6 different algorithms

Overview

Breast Cancer Classification

  Using SKLearn breast cancer dataset which contains 569 examples and 32 features classifying has been made with 6 different algorithms. The metrics below have been used to determine these algorithms performance.

  • Accuracy
  • Precision
  • Recall
  • F Score

Accuracy may produce misleading results so because of that I also added some metrics which some of them are more reliable (e.g. F Score).

Algorithms

  Logistic regression, SVM (Support Vector Machines), decision trees, random forest, naive bayes, k-nearest neighbor algorithms have been used and for each of them metrics are calculated and results are shown.

Data Preprocessing

  The dataset contains no missing rows or columns so we can start feature selection. To do that I used correlation map to show the correlation between features. And I eliminated mostly correlated features like perimeter_mean and perimeter_worst. After this process we have 18 features.

image

Then we apply data normalization and our data is ready for classification.

# Data normalization
standardizer = StandardScaler()
X = standardizer.fit_transform(X)

Train and Test Split

I have split my dataset as %30 test, % 70 training and set random_state parameter to 0 as shown.

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

After splitting dataset, I created dictionaries for algorithms and metrics. And in one for loop every model trained and tested.

models = {'Logistic Regression': LogisticRegression(), 'Support Vector Machines': LinearSVC(),
          'Decision Trees': DecisionTreeClassifier(), 'Random Forest': RandomForestClassifier(),
          'Naive Bayes': GaussianNB(), 'K-Nearest Neighbor': KNeighborsClassifier()}

accuracy, precision, recall, f_score = {}, {}, {}, {}

for key in models.keys():
    # Fit the classifier model
    models[key].fit(X_train, y_train)

    # Classification
    classification = models[key].predict(X_test)

    # Calculate Accuracy, Precision, Recall and F Score Metrics
    accuracy[key] = accuracy_score(classification, y_test)
    precision[key] = precision_score(classification, y_test)
    recall[key] = recall_score(classification, y_test)
    f_score[key] = f1_score(classification, y_test)

Results

As you can see the figure below, most successful classification algorithm seems to logistic regression. And decision tress has the worst performance.

image

To see the values algorithms got for each metric see the table below.

Algorithm Accuracy Precision Recall F Score
Logistic Regression 0.97 0.95 0.96 0.96
SVM 0.95 0.95 0.93 0.94
Decision Trees 0.86 0.84 0.80 0.82
Random Forest 0.94 0.93 0.90 0.92
Naive Bayes 0.90 0.87 0.85 0.86
K-Nearest Neighbor 0.91 0.85 0.91 0.88

Conclusion

I have tuned few parameters for example training and test size, random state and most of the algorithms performed close enough to each other. For different datasets this code can be used. You may need to change feature selection part and if your dataset has missing values you should fill in these values as well. Other than these things you can perform classification with different kind of algorithms.

You might also like...
Implementation of different ML Algorithms from scratch, written in Python 3.x
Implementation of different ML Algorithms from scratch, written in Python 3.x

Implementation of different ML Algorithms from scratch, written in Python 3.x

LibRerank is a toolkit for re-ranking algorithms. There are a number of re-ranking algorithms, such as PRM, DLCM, GSF, miDNN, SetRank, EGRerank, Seq2Slate.

LibRerank LibRerank is a toolkit for re-ranking algorithms. There are a number of re-ranking algorithms, such as PRM, DLCM, GSF, miDNN, SetRank, EGRer

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

Light Gradient Boosting Machine LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed a

CrayLabs and user contibuted examples of using SmartSim for various simulation and machine learning applications.

SmartSim Example Zoo This repository contains CrayLabs and user contibuted examples of using SmartSim for various simulation and machine learning appl

This repository has datasets containing information of Uber pickups in NYC from April 2014 to September 2014 and January to June 2015. data Analysis , virtualization and some insights are gathered here

uber-pickups-analysis Data Source: https://www.kaggle.com/fivethirtyeight/uber-pickups-in-new-york-city Information about data set The dataset contain

machine learning model deployment project of Iris classification model in a minimal UI using flask web framework and deployed it in Azure cloud using Azure app service
machine learning model deployment project of Iris classification model in a minimal UI using flask web framework and deployed it in Azure cloud using Azure app service

This is a machine learning model deployment project of Iris classification model in a minimal UI using flask web framework and deployed it in Azure cloud using Azure app service. We initially made this project as a requirement for an internship at Indian Servers. We are now making it open to contribution.

Examples and code for the Practical Machine Learning workshop series

Practical Machine Learning Workshop Series Practical Machine Learning for Quantitative Finance Post conference workshop at the WBS Spring Conference D

Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

Prophet: Automatic Forecasting Procedure Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends ar

LILLIE: Information Extraction and Database Integration Using Linguistics and Learning-Based Algorithms

LILLIE: Information Extraction and Database Integration Using Linguistics and Learning-Based Algorithms Based on the work by Smith et al. (2021) Query

Owner
Mert Sezer Ardal
Mert Sezer Ardal
Interactive Web App with Streamlit and Scikit-learn that applies different Classification algorithms to popular datasets

Interactive Web App with Streamlit and Scikit-learn that applies different Classification algorithms to popular datasets Datasets Used: Iris dataset,

Samrat Mitra 2 Nov 18, 2021
A naive Bayes model for cancer classification using a set of documents

Naivebayes text classifcation model for cancer and noncancer documents Author: Alex King Purpose Requirements/files included How to use 1. Purpose The

Alex W King 1 Nov 24, 2021
Turning images into '9-pan' palettes using KMeans clustering from sklearn.

img2palette Turning images into '9-pan' palettes using KMeans clustering from sklearn. Requirements We require: Pillow, for opening and processing ima

Samuel Vidovich 2 Jan 1, 2022
Multiple Linear Regression using the LinearRegression class from sklearn.linear_model library

Multiple-Linear-Regression-master - A python program to implement Multiple Linear Regression using the LinearRegression class from sklearn.linear model library

Kushal Shingote 1 Feb 6, 2022
In this Repo a simple Sklearn Model will be trained and pushed to MLFlow

SKlearn_to_MLFLow In this Repo a simple Sklearn Model will be trained and pushed to MLFlow Install This Repo is based on poetry python3 -m venv .venv

null 1 Dec 13, 2021
50% faster, 50% less RAM Machine Learning. Numba rewritten Sklearn. SVD, NNMF, PCA, LinearReg, RidgeReg, Randomized, Truncated SVD/PCA, CSR Matrices all 50+% faster

[Due to the time taken @ uni, work + hell breaking loose in my life, since things have calmed down a bit, will continue commiting!!!] [By the way, I'm

Daniel Han-Chen 1.4k Nov 23, 2022
Machine learning template for projects based on sklearn library.

Machine learning template for projects based on sklearn library.

Janez Lapajne 17 Oct 28, 2022
Test symmetries with sklearn decision tree models

Test symmetries with sklearn decision tree models Setup Begin from an environment with a recent version of python 3. source setup.sh Leave the enviro

Rupert Tombs 2 Jul 19, 2022
Napari sklearn decomposition

napari-sklearn-decomposition A simple plugin to use with napari This napari plug

null 1 Sep 1, 2022
A chain of stores, 10 different stores and 50 different requests a 3-month demand forecast for its product.

Demand-Forecasting Business Problem A chain of stores, 10 different stores and 50 different requests a 3-month demand forecast for its product.

Ayşe Nur Türkaslan 3 Mar 6, 2022