New research: Deep Learning for Image Analysis

We discussed this research as part of our virtual event on Wednesday, July 24th; you can watch the replay here!
Convolutional Neural Networks (CNNs or ConvNets) excel at learning meaningful representations of features and concepts within images. These capabilities make CNNs extremely valuable for solving problems in the image analysis domain.

Read Full Story

Random forest interpretation with scikit-learn

In one of my previous posts I discussed how random forests can be turned into a “white box”, such that each prediction is decomposed into a sum of contributions from each feature i.e. (prediction = bias + feature_1 contribution + … + feature_n contribution).I’ve a had quite a few requests for code to do this.

Read Full Story

Answering Every Marketer’s Dilemma – Which attribution model to choose? All about Fractional Attribution – Part 2

  In the last blog, we have discussed the various attribution models available in most of the sophisticated analytics tools and we now know that considering the position and the frequency of occurrence of channels can be instrumental in designing…
The post Answering Every Marketer’s Dilemma – Which attribution model to choose?

Read Full Story