IBM SPSS and Entity Analytics at work

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AI, ML, NN and DL: a visual explanation

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There appears to be a lot of confusion between the terms Artificial Intelligence (AI), Machine Learning (ML), Neural Networks (NN) and Deep Learning (DL).  Based on research from various popular blogs and articles, here is my attempt at a simple visual explanation:
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Understanding And Interpreting Gain And Lift Charts

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Lift and Gain Charts are a useful way of visualizing how good a predictive model is. In SPSS, a typical gain chart appears as follows: In today's post, we will attempt to understand the logic behind generating a gain chart and then discuss how gain and lift charts are interpreted. To do this, we will use the example of a direct mailing company. Let us assume that based on experience, the company knows that the average response rate on its direct mail campaigns is 10%. Let us further make the following assumptions: * Cost per ad mailed = $1 * Return per response = $50 Additionally, let us assume that the company mails out ads in lots of 10,000. Based on these assumptions, if the company mails out 100,000 ads, a table summarizing the results it would obtain from this campaign is provided below: Now let us assume that the company uses SPSS Modeler to develop a predictive model using data from previous campaigns. "Response / No Response" is identified as the &
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If you thought the coefficient of alienation referred to the hostility I receive from my family as I update my blog on a Saturday afternoon, I would not fault you too much.  However, this is a blog about predictive analytics which is based on Statistics.  So let's keep that in mind as we understand what the "Coefficient of Alienation" means. Apart from being one of the coolest sounding Statistical terms, the Coefficient of Alienation measures the proportion of variation in the outcome not “explained” by the variables on the right-hand side of a simple linear regression (ordinary least squares) equation. The Coefficient of Alienation is also known as the Coefficient of Non-Determination since the formula for calculating it is: where: is the Coefficient of Determination. And now before my personal (and non-Statistical) Coefficient of Alienation reaches the point of no return, I will bring this post to an end.
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