Due to Machine Learning development being in high demand in recent years, It represents a dominant area for researching and learning in the Information Technologies sector, while constantly upgrading existing models used in other sectors and industries.Despite this, model variance and bias in the collected data (and its prevention) remains as one of the main problems in Machine Learning development . Let us simplify this by defining the subjects:
Bias: an error in the Machine Learning training dataset.
Variability: variance in Machine learning model prediction.
The difference between bias and variability is that bias can be seen before including a dataset in Machine Learning model training, while variance can be stated during or after training (Machine Learning model development). Variance simply shows how adjustable the model is when using only a given training dataset. Bias, on the other hand,shows the matching percentage between the model and training dataset included. If the model contains a lot of bias, it can’t be verified as sustainable and trustworthy for further development. Variance and bias in Machine Learning development are conversely connected – models with high biases will have low variance and vice versa. Both of those subjects can be described under the term “model flexibility.” A model that doesn’t match a dataset with a high bias will create an inflexible model with a low variance, resulting in an insufficiently stable Machine Learning model. Unstable models can’t be used in further development, so insufficient and poor training datasets represent a huge loss of time and money for companies and a big step back in the Machine Learning development process. Models with a high percentage of bias usually have a high error rate, and generalized and oversimplified data that fail to meet data trends. Models with high variability are often complex models that contain noisy datasets that try to bring all data points as close as possible.
Open Source
Anton Knight 
Kayley Marshall 