Week 6- Breast Cancer Detection
Hello everyone!
This is our sixth blog about our Machine Learning Course Project on Breast Cancer Detection. Last week we used the Naïve Bayes classifier algorithm in our data set. This week we used the Neural network.
Neural Network
Neural networks are a set of algorithms, modeled loosely after the human brain, that is designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated.
Neural networks help us cluster and classify. You can think of them as a clustering and classification layer on top of the data you store and manage. They help to group unlabeled data according to similarities among the example inputs, and they classify data when they have a labeled dataset to train on. (Neural networks can also extract features that are fed to other algorithms for clustering and classification; so you can think of deep neural networks as components of larger machine-learning applications involving algorithms for reinforcement learning, classification, and regression.)
Our neural network parameters:
Learning rate: 0.001
Percentage of a training set to use as validation: %20
Activation function: Sigmoid
Loss function: Binary cross-entropy loss
Epoch: 2000
Our accuracy:0.979
Our video presentation link: https://www.youtube.com/watch?v=rjzNv3u8DcI&t=4s
Previous posts:
Week 5: https://medium.com/bbm406f19/week-5-breast-cancer-detection-c7acbb1e010
References:
