Deep Learning: Providing Big Benefits Like Predictive Modeling

Deep learning networks can learn complex patterns in data, and this makes them very effective at tasks such as image recognition, natural language processing and predictive modeling.

Predictive modeling using deep learning is particularly useful.

In short, predictive modeling is a statistical technique using machine learning and data mining to predict and forecast likely future outcomes with the aid of historical and existing data. It works by analyzing current and historical data and projecting what it learns on a model generated to forecast likely outcomes.

Predictive modeling can be used to predict just about anything, from TV ratings and a customer’s next purchase to credit risks and corporate earnings.

Predictive models make assumptions based on what has happened in the past and what is happening now. If incoming, new data shows changes in what is happening now, the impact on the likely future outcome must be recalculated, too.

Most predictive models work fast and often complete their calculations in real time. That’s why banks and retailers can, for example, calculate the risk of an online mortgage or credit card application and accept or decline the request almost instantly based on that prediction.

Some predictive models are more complex and are used in?computational biology?and quantum computing.

There are many ways of classifying predictive models and in practice multiple types of models may be combined for best results. The most salient distinction is between?unsupervised versus supervised models.

• Unsupervised models use traditional statistics to classify the data directly, using techniques like?logistic regression, time series analysis and decision trees.
• Supervised models use newer machine learning techniques such as neural networks to identify patterns buried in data that has already been labeled.

The biggest difference between these approaches is that with supervised models more care must be taken to properly label data sets upfront.

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