Validating Models: A Key Step on the Path to Artificial Intelligence

Validating Models: A Key Step on the Path to Artificial Intelligence

Validating Models: A Key Step on the Path to Artificial Intelligence

To stay competitive in a digital economy, businesses increasingly need to move beyond simple reporting and descriptive analytics to a more predictive approach that puts artificial intelligence (AI) strategies to work to engage with customers in new ways.

So how can you find a practical way to start applying AI in your business? One path forward follows three steps: leverage predictive models to improve how you engage with customers, put machine learning to work to improve those models, and then validate your models. In an earlier blog, I explored the dynamics of predictive analytics and machine learning. In this post, I will focus on the validation of predictive models First let me provide a quick overview of predictive analytics and machine learning, and explain why validation is important when you apply these approaches.

Predictive analytics is about using algorithms to predict the result of a measurement that you can’t make, based on measurements that you can make. Why can’t you make the measurements you need? Perhaps you are trying to predict what’s going to happen next. Unless you have a time machine (in which case you are probably wasting your time on analytics!), you can’t measure something that hasn’t happened yet. Forecasting customer behavior, business trends and future events is always of value in running a business. Often, it is not possible to measure something in the present because of practical considerations. For example, suppose you want to present a lunch ordering application for your chain of lunchtime food delivery restaurants. Your best chance of getting someone’s attention is when they are hungry and online. Obviously, it’s not possible to ask everyone who is online whether they are hungry, so you need to infer their hunger status from their behavior: Is it lunchtime? Are they looking up lunch options? The goal of your predictive analytics in this case is to infer who is most likely to respond to your product or offer, based on the data you are able to collect. In general, your predictive analytics application can take into account a customer’s past account history, past conversations with the call center, behavior of “similar” customers, the location of the customer, and even what’s trending on social media at any given time. Good predictive analytics will give you the best chance of a mutually beneficial interaction with your customer.

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The challenge is that, compared with diagnostic and descriptive analytics, predictive analytics is a new world. You are actually making predictions or inferences based on past data. To be successful at this, and to avoid making grossly inaccurate predictions (or at least understand how accurate your predictions may be), you will need to validate your models to ensure that you have discovered useful, generalizable patterns in your data.

So how do we build a good predictive analytics application? Two words: Machine learning (ML). Predictive analytics leverages machine learning algorithms that build systems that learn iteratively from data, identify patterns, and predict future results. Machine learning algorithms organize things into meaningful groups, find unusual patterns in data, and can predict the next data point in a time series.

There’s an important caveat to call out here. Machine Learning algorithms learn from data, but on their own they are not great at distinguishing between memorizing past data and finding generalizable underlying patterns in data. When learning from past data for predictive analytics the goal is to generalize, not memorize. Poorly constructed ML algorithms can memorize all of the data in a huge data set, resulting in a system that is very poor at predicting the outcome of any situation they haven’t already seen in the past. Instead, you need to train ML algorithms to focus on a limited number of free parameters that enable reliable predictions about the future. ML algorithms that generalize well are called “robust” algorithms.

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