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1

What is Predictive Marketing?

Do You Even Need Predictive Marketing? It comes down to insights versus actions. Predictive Marketing is the umbrella term encompassing processes that rely on AI and machine learning to drive actions with accurate predictions. This is in contrast to traditional marketing, which has been built on descriptive and diagnostic processes. Traditional Analysis Business questions are formulated […]

2

How to Pick AutoML Use Cases

Hint: Focus on key performance indicators. Automated Machine Learning (AutoML) can help you make better and more timely decisions by detecting signals in data that would be impossible to see with conventional analysis. To make the most of this power to see the future, attack your most important performance indicators. Remember that AutoML delivers specific, […]

3

AutoML vs. Data Scientists

A little respect is due—in both directions. Can Automated Machine Learning (AutoML) beat serious data scientists in producing accurate predictions? People who understand data science and programming deeply, when armed with all the tools and computer resources and time they need, are nearly always able to produce better models than generalized, self-programming AutoML systems. There […]

4

How “Auto” Is That AutoML?

Some AutoML systems are more automatic than others. AutoML stands for Automated Machine Learning, meaning streamlining the end-to-end process of solving problems with machine learning. Steps that need to be automated include: Data Preparation (type and dependency) Feature Engineering Model Algorithm Selection Training Hyperparameter Tuning Model Evaluation Production Processing and Deployment In practice, there is […]

5

How to Prove AI Value to Skeptics

Make a prediction that comes true. AI has not taken its seat at the decision making table for many organizations that would reap big benefits from it. That’s easy to understand, since the terminology—Artificial Intelligence, machine learning, robotic assistants, and the like—are conflated in stories and ads to the point of being meaningless. Nearly every system […]

6

How AutoML Beats Scoring Formulas

AutoML’s ability to detect patterns and predict can out-perform algebraic formulas and Boolean logic in common tasks. Anyone who has written a formula in Excel or adjusted parameters in an online application knows how even the smallest change can produce dramatically different results. The power of mass calculation is exactly what puts algebraic and Boolean logic […]

7

What Are Machine Learning Hyperparameters and How Are They Used?

Parameters are functions of training data. Hyperparameters are settings used to tune model algorithm performance. In Automated Machine Learning (AutoML), data sets containing known outcomes are used to train models to make predictions. The actual values in training data sets never directly become parts of models. Instead, AutoML algorithms learn patterns in the features (columns) and […]

8

What is Bias in Machine Learning?

Bias occurs when ML does not separate the true signal from the noise in training data. Biases in AI systems make headlines for results such as favoring gender in hiring, recommending loans based on ethnicity, or recognizing faces differently based on race. Some of these cases were due to biases baked into the algorithms written by (human) […]

9

Monte Carlo Simulation vs. Machine Learning

Simulation uses models constructed by experts to predict probabilities. Machine Learning builds its own models to predict future outcomes. Monte Carlo (the place) is the iconic capital of gambling—an endeavor that relies exclusively on chance probabilities to determine winners and losers. Monte Carlo (the method) employs random inputs to models to make predictions on how […]

10

Data Mining vs. Machine Learning

Data Mining describes patterns, correlations, and anomalies in data. Mines are not the best analogies for the processes referred to as Data Mining. Never mind that we call data storage places bases, warehouses, and lakes. Extraction of raw data material is not the goal of data mining, but rather identifying characteristics within data sets that can be […]