Combining the concepts of logistic regression and a neural network to perform binary classification tasks
Logistic regression is a statistical model that is primarily used for binary classification tasks and MLP is feedforward neural net that consists of multiple layers of nodes that incuded input layer, multi-hidden layers, and output layer
- Implementation of a neural network designed to classify breast cancer samples into malignant or benign categories
- Feedforward Processing: Calculates the linear combination of inputs and weights (weighted sum), adds bias, and applies the sigmoid activation function to estimate the probability of belonging to a particular class.
- Backpropagation: Adjusts weights and biases based on the error between predicted and actual values, using a simple gradient descent approach.
- Prediction: Classifies samples by applying the trained model to new data, returning a binary output based on a threshold.
