Now that we have training and test datasets sampled, initially preprocessed and available in the data grid, we can close Web Notebook and start experimenting with different techniques and algorithms by submitting Spark applications. For our first baseline approach let's take a single feature device_conn_type and logistic regression algorithm: We will explain a little bit more what happens here. At first, we load the training dataset from the data grid, which we prepared and saved earlier with Web Notebook. Then we use StringIndexer and OneHotEncoder to map a column of categories to a column of binary vectors. For example, with 4 categories of device_conn_type, an input value of the second category would map to an output vector of [0.0, 1.0, 0.0, 0.0, 0.0].

This blog post will provide an introduction into using machine learning algorithms with InsightEdge. We will go through an exercise to predict mobile advertisement click-through rate with Avazu's dataset. There are several compensation models in online advertising industry, probably the most notable is CPC (Cost Per Click), in which an advertiser pays a publisher when the ad is clicked. Search engine advertising is one of the most popular forms of CPC. It allows advertisers to bid for ad placement in a search engine's sponsored links when someone searches on a keyword that is related to their business offering.