This repo contains the notebooks and scripts written in the process of mastering Scikit Learn library for ML. Note repo still under construction since the learning process is still going on.
Classification of the classic breast cancer Wisconsin dataset using SVM Classifier with scikit learn. They accuracy reached was close to 95.8% . precision recall f1-score support
malignant 0.96 0.93 0.94 54
benign 0.96 0.98 0.97 89
accuracy 0.96 143
macro avg 0.96 0.95 0.96 143
weighted avg 0.96 0.96 0.96 143
We use the concepts of SVM to build a face classifer. Further we also use PCA in order to make the classifer better.The classification improved from 85 to 90 percent due PCA.The dataset used is Labeled Faces in the Wild from scikit-learn. It consists of more than 13,000 curated face images of more than 5,000 famous people. Each class has various numbers of image samples.
precision recall f1-score support
Colin Powell 0.89 0.88 0.88 64
Donald Rumsfeld 0.84 0.81 0.83 32
George W Bush 0.88 0.93 0.90 127
Gerhard Schroeder 0.84 0.72 0.78 29
Tony Blair 0.91 0.88 0.89 33
accuracy 0.88 285
macro avg 0.87 0.84 0.86 285
weighted avg 0.88 0.88 0.88 285
precision recall f1-score support
Colin Powell 0.91 0.95 0.93 64
Donald Rumsfeld 0.77 0.84 0.81 32
George W Bush 0.95 0.91 0.93 127
Gerhard Schroeder 0.81 0.86 0.83 29
Tony Blair 0.93 0.85 0.89 33
accuracy 0.90 285
macro avg 0.87 0.88 0.88 285
weighted avg 0.90 0.90 0.90 285
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