Explainable AI or XAI is a sub-category of AI where the decisions made by the model can be interpreted by humans, as opposed to "black box" models. As AI moves from correcting our spelling and targeting ads to driving our cars and diagnosing patients, the need to verify and justify the conclusions being reached is beginning to be prioritised. To begin to delve into the field, lets look at one simple XAI model: the decision tree. Decision trees can be easily read and even mimic a human approach to decision making by breaking the choice into many small sub-choices. A simple example is how one may evaluate local universities when the leave high school.

I agree with the idea of using boosting algorithms is better but not enough in practice. SMOTE would be a good starting point (definitely I would opt for a over-sampling strategy) but there are others. Here you can find a nice implementation of solutions for imbalanced data in python (scikit-learn-contrib). The success of any of these techniques depend largely on the nature of your data. Therefore, I would suggest you try different approaches and see how they affect your results.

This paper proposes a set of new methods to estimate inequality of opportunity based on conditional inference regression trees. It illustrates how these methods represent a substantial improvement over existing empirical approaches to measure inequality... See More This paper proposes a set of new methods to estimate inequality of opportunity based on conditional inference regression trees. It illustrates how these methods represent a substantial improvement over existing empirical approaches to measure inequality of opportunity. First, the new methods minimize the risk of arbitrary and ad hoc model selection. Second, they provide a standardized way to trade off upward and downward biases in inequality of opportunity estimations.

Random Forest is a flexible, easy to use machine learning algorithm that produces, even without hyper-parameter tuning, a great result most of the time. It is also one of the most used algorithms, because it's simplicity and the fact that it can be used for both classification and regression tasks. In this post, you are going to learn, how the random forest algorithm works and several other important things about it. Random Forest is a supervised learning algorithm. Like you can already see from it's name, it creates a forest and makes it somehow random.

Today, I'm going to explain in plain English the top 10 most influential data mining algorithms as voted on by 3 separate panels in this survey paper. Once you know what they are, how they work, what they do and where you can find them, my hope is you'll have this blog post as a springboard to learn even more about data mining. What are we waiting for? We also provide interesting resources at the end. In order to do this, C4.5 is given a set of data representing things that are already classified.

This article is by Jitesh Shah, a data & stats jockey in perpetual beta, located in Fremont, California. This article includes the data set and Python code. Wouldn't it be nice if defects and product failures can be predicted in advance. We've got the data on attributes and design features and manufacturing processes that come together and creates that product and we have defect and failure rate data so all we got to do is connect the two and use that to predict which set of features and attributes and processes in combination cause these defects. That was probably a non-trivial endeavor in the past but now with the ability to store and process vast amounts of data (no secret there), no big deal.

We introduce a novel incremental decision tree learning algorithm, Hoeffding Anytime Tree, that is statistically more efficient than the current state-of-the-art, Hoeffding Tree. We demonstrate that an implementation of Hoeffding Anytime Tree---"Extremely Fast Decision Tree", a minor modification to the MOA implementation of Hoeffding Tree---obtains significantly superior prequential accuracy on most of the largest classification datasets from the UCI repository. Hoeffding Anytime Tree produces the asymptotic batch tree in the limit, is naturally resilient to concept drift, and can be used as a higher accuracy replacement for Hoeffding Tree in most scenarios, at a small additional computational cost.

The lending and credit scoring sector have more data than ever before at their disposal. How they leverage this data to create value for their clients and social impact determines the outcomes they can achieve in the financial services space. In 1959, Arthur Samuel, a pioneer in the field of machine learning (ML) and artificial intelligence during an era when computers filled an entire building, defined machine learning as "a field of study that gives computers the ability to learn without being explicitly programmed." During a recent keynote, Microsoft CEO Satya Nadella referred to data used in this context as "the new electricity," calling our current era a "fourth industrial revolution" following steam, electricity and digital technology. Scott Guthrie, Microsoft executive vice president, also acknowledged that data is "enabling every business to be the disrupters of their industry by harnessing the power to drive insight from this data."

Random forest is a highly versatile machine learning method with numerous applications ranging from marketing to healthcare and insurance. It can be used to model the impact of marketing on customer acquisition, retention, and churn or to predict disease risk and susceptibility in patients. Random forest is capable of regression and classification. It can handle a large number of features, and it's helpful for estimating which of your variables are important in the underlying data being modeled. This is a post about random forests using Python.

The study of ML algorithms has gained immense traction post the Harvard Business Review article terming a'Data Scientist' as the'Sexiest job of the 21st century'. So, for those starting out in the field of ML, we decided to do a reboot of our immensely popular Gold blog The 10 Algorithms Machine Learning Engineers need to know - albeit this post is targetted towards beginners. ML algorithms are those that can learn from data and improve from experience, without human intervention. Learning tasks may include learning the function that maps the input to the output, learning the hidden structure in unlabeled data; or'instance-based learning', where a class label is produced for a new instance by comparing the new instance (row) to instances from the training data, which were stored in memory. 'Instance-based learning' does not create an abstraction from specific instances. Supervised learning can be explained as follows: use labeled training data to learn the mapping function from the input variables (X) to the output variable (Y).