This is a comparison of available binary classifiers in Scikit-Learn on the breast cancer (BC) dataset. The BC dataset comes with the Scikit-Learn package itself. Scope: QC comparison of Scikit-Learn binary classifiers - Logistic Regression (LR), Extra Trees (ET), Gradient Boosting (GB), Random Forest (RF), and KNN. Content: reading input data, importing key libraries, Exploratory Data Analysis (EDA), ML data preparation (scaling and test/train/target/features data splitting), ML model training, test data predictions, and classification QC analysis using available metrics and Scikit-Plot curves. Cumulative gains and lift curves (ML performance) are identical for all classifiers PCA: Silhouette score 0.116, number of clusters is 3 (elbow plot), 0.982 explained variance ratio for first 1 components. ML classification report: ROC AUC score 0.98 (KNN), F1-score 0.97 (LR), FP=3% (LR). PCA 2D projection: good separation of classes 0 and 1. #breastcancer #BreastCancerAwarenessMonth #breastcancerawareness #womenhealth #CancerAwarenessMonth #machinelearning #artificialintelligence #datascience #kaggle #python #scikitlearn #datavisualization #classifications #model #Training #testing #validation #confidence #diagnosis #diagnostics #predictions #healthcare #health #publichealth #screening Explore More: https://wp.me/pdMwZd-2SX Youtube link: https://youtu.be/BRN3W_HR3FA
import scikitplot as skplt
import sklearn from sklearn.datasets import load_digits, load_boston, load_breast_cancer from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, GradientBoostingClassifier, ExtraTreesClassifier from sklearn.linear_model import LinearRegression, LogisticRegression from sklearn.cluster import KMeans from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import sys import warnings warnings.filterwarnings("ignore")
print("Scikit Plot Version : ", skplt.version) print("Scikit Learn Version : ", sklearn.version) print("Python Version : ", sys.version)
%matplotlib inline
Scikit Plot Version : 0.3.7 Scikit Learn Version : 1.1.3 Python Version : 3.9.13 (main, Aug 25 2022, 23:51:50)