Weight assignment to make model more precise

## Practice full name

Make a particular assignment to the weights in order to make the model more precise in the predictions

## About

A model that predicts with a too high error rate is certainly not satisfactory. On the contrary, the prediction errors due to the too high error rate could be confused with any discrimination, in case the prediction is wrong due to the sensitive features, when perhaps the system does not make any. Therefore, it is necessary to modify the model weights in order to improve the predictions made, reducing the error rate. It's important to not make the model too precise, otherwise, overfitting will occur.

## What the crowd is saying

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## Involved topics

### What is a weight?

Weight is the parameter within a neural network that transforms input data within the network's hidden layers. As an input enters the node, it gets multiplied by a weight value and the resulting output is either observed, or passed to the next layer in the neural network. Often the weights of a neural network are contained within the hidden layers of the network [1].

### Weight initialization

Weight initialization is an important design choice when developing deep learning neural network models. Historically, weight initialization involved using small random numbers, although over the last decade, more specific heuristics have been developed that use information, such as the type of activation function that is being used and the number of inputs to the node. These more tailored heuristics can result in more effective training of neural network models using the stochastic gradient descent optimization algorithm [2].

### Underfitting and overfitting

Underfitting refers to a model that can neither model the training data nor generalize to new data. An underfit machine learning model is not a suitable model and will be obvious as it will have poor performance on the training data.

Overfitting refers to a model that models the training data too well. Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means that the noise or random fluctuations in the training data is picked up and learned as concepts by the model. The problem is that these concepts do not apply to new data and negatively impact the models ability to generalize [3].

### What is a sensitive feature?

Sensitive features (or protected attributes) are attributes for which the machine learning algorithm could discriminate subjects. Some common examples of this attributes are age, disability, national origin, race/color, religion, sex. Some cases where discrimination has occurred are facial recognition and hiring. The theory holds that oversampling and undersampling techniques can cause discrimination [4].

## Sources

1. Weight (Artificial Neural Network)
2. Weight Initialization for Deep Learning Neural Networks
3. Overfitting and Underfitting With Machine Learning Algorithms
4. Handling Discriminatory Biases in Data for Machine Learning

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