The Variance-Bias-Trade-off can be illustrated in machine learning with the terms of overfitting and underfitting. In general, it is the case that if the variance is high, the bias is low, and vice versa. Variance refers to the value by which a prediction would change if the same model were trained on a different data set.
A high variance means that small changes to the data would cause a large change in the forecast (overfitting). The model tends to fit closely with the data and to consider random noise as important and thus incorporate it into the model.
In contrast, bias describes the errors that occur when predictions are made with a model (underfitting). A high bias indicates that the model is too general to make accurate predictions. For example, if a non-linear relationship was estimated with a linear regression, it would not produce reliable predictions and would therefore be underfitted, but would have a low variance. Small changes in data would have little effect on the linear estimate.
All in all this shows that the Variance-Bias-Trade-off occurs in every model estimation. The model should describe the data so precisely that a generalisation to unseen data is still possible. The model evaluation in Machine Learning aims to balance this trade-off.
You can find a detailed and illustrative blog post on TowardsDataScience.
More resources about machine learning
How machine learning benefits from data integration
The causal chain “data integration-data quality-model performance” describes the necessity of effective data integration for easier and faster implementable and more successful machine learning. In short, good data integration results in better predictive power of machine learning models due to higher data quality.
From a business perspective, there are both cost-reducing and revenue-increasing effects. The development of the models is cost-reducing (less custom code, thus less maintenance, etc.). Revenue increasing is caused by the better predictive power of the models leading to more precise targeting, cross- and upselling, and more accurate evaluation of leads and opportunities – both B2B and B2C. You can find a detailed article on the topic here:
How to use machine learning with the Integration Platform
You can make the data from your centralized Marini Integration Platform available to external machine learning services and applications. The integration works seamlessly via the HubEngine or direct access to the platform, depending on the requirements of the third-party provider. For example, one vendor for standard machine learning applications in sales is Omikron. But you can also use standard applications on AWS or in the Google Cloud. Connecting to your own servers is just as easy if you want to program your own models there.
If you need support on how to integrate machine learning models into your platform, please contact our sales team. We will be happy to help you!
Frequent applications of machine learning in sales
Machine learning can support sales in a variety of ways. For example, it can calculate closing probabilities, estimate cross-selling and up-selling potential, or predict recommendations. The essential point here is that the salesperson is supported and receives further decision-making assistance, which he can use to better concentrate on his actual activity, i.e., selling. For example, the salesperson can more quickly identify which leads, opportunities or customers are most promising at the moment and contact them. However, it remains clear that the salesperson makes the final decision and is ultimately only facilitated by machine learning. In the end, no model sells, but still the human being.
Here you will find a short introduction to machine learning and the most common applications in sales.