Maybe a cautious approach to a bigger version of the Monolith (2km long) in a POD that is only 2m in diameter is advisable. What do we know about how Monoliths react to stimuli?

In this post I am going to discuss some features of Regression Trees an Random Forests. Regression Trees are know to be very unstable, in other words, a small change in your data may drastically change your model. The Random Forest uses this instability as an advantage through bagging (you can see details about bagging here) resulting on a very stable model. The first question is how a Regression Tree works. Suppose, fore example, that we have the number of points scored by a set of basketball players and we want to relate it to the player's weight an height.

In this post I'm going to try to do three things: Code and links to the package are included at the bottom of this post. A decision tree is fully interpretable. The coefficients or branches of the model tells you the'why' of each prediction. For example, take the following decision tree, that predicts the likelihood of an employee leaving the company. Predictions made using this tree are entirely transparent -- i.e. you can say exactly how each feature has influenced the prediction.

Twitter is a dynamic world that works in a confined space of 140 characters but the impact it can have on an audience is huge. Just like fake news, fake followers can be detrimental to the image of businesses and even to those individual users who rely heavily on social media marketing. Since Twitter places significant constraints on the type of communication that is possible, it becomes easier for bots to reconstruct the human behavior that is demonstrated in the limited Twitter dimension. Hence, results that we obtain in the form of social media metrics can often get skewed. So why is the detection of Social Bots important for social media users?

This video shows a general review of the analytics capabilities of the KNIME Analytics Platform. Here we only mention: Random Forest, Deep Learning, Gradient Boosted Trees, Bagging and Boosting for ensemble methods, Decision Trees, Neural Networks, Logistic Regression, how to build your own ensemble model, and external integrations as Weka, H2O, R, and Python. This is what we show here, which for time reasons, is of course incomplete. Download and install KNIME Analytics Platform (https://www.knime.com/downloads) to explore the constantly growing set of machine learning and statistics algorithms available to analyze your data.

The random forest has been a burgeoning machine learning technique in the last few years. It is a non-linear tree-based model that often provides accurate results. However, being mostly black box, it is oftentimes hard to interpret and fully understand. In this blog, we will deep dive into the fundamentals of random forests to better grasp them. We start by looking at the decision tree--the building block of the random forest.

Are you interested in the cutting edge of artificial intelligence? Do you want to understand how your phone can understand your voice? Or do you perhaps want to learn what happens when statistics, biology, and psychology combine with computer science? Maybe you just want to know how a robot can actually learn to walk? If these questions are on your mind then the answer you are looking for is machine learning.

IEEE International Conference on Data Mining identified 10 algorithms in 2006 using surveys from past winners and voting. This is a list of those algorithms a short description and related python resources. The detailed paper is given here. C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan. The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier.

Random survival forest can be extremely time consuming for large data set. In this paper we propose few computationally efficient algorithms in prediction of survival function. We explore the behavior of the algorithms for different cancer data sets. Our construction includes right censoring data too. We have also applied the same for competing risk survival function.

The previous posts in this series have covered several ways that business leaders can use to understand and explore how Artificial Intelligence can impact their business. We saw that there are several key ways in which AI advances can improve human productivity in organizations. The last two articles dived into Distillation: automating the path to value, and Categorization: managing data at scale. Prediction is applying AI approaches to learn from past (and possibly other) data to predict what will happen. An excellent example is the Spam Filter algorithm used in email systems. Based on past email that has been identified as Spam or not Spam (frequently called Ham) then a predictive model can be developed that will predict whether a new, never before seen, email is Spam or Ham.