In this post we're going to discuss a commonly used machine learning model called decision tree. Decision trees are preferred for many applications, mainly due to their high explainability, but also due to the fact that they are relatively simple to set up and train, and the short time it takes to perform a prediction with a decision tree. Decision trees are natural to tabular data, and, in fact, they currently seem to outperform neural networks on that type of data (as opposed to images). Unlike neural networks, trees don't require input normalization, since their training is not based on gradient descent and they have very few parameters to optimize on. They can even train on data with missing values, but nowadays this practice is less recommended, and missing values are usually imputed.

Let's assume that we have a labeled dataset with 10 samples in total. What the Decision Trees do is simple: they find ways to split the data in a way such as that separate as much as possible the samples of the classes (increasing the class separability). In the above example, the perfect split would be a split at x 0.9 as this would lead to 5 red points being at the left side and the 5 blue at the right side (perfect class separability). Each time we split the space/data like that, we actually build a decision tree with a specific rule. Here we initially have the root node containing all the data and then, we split the data at x 0.9 leading to two branches leading to two leaf nodes.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. This is going to be part 4 of the Explaining Machine Learning Algorithms To A 10-Year Old series.

The Decision Tree is a machine learning algorithm that takes its name from its tree-like structure and is used to represent multiple decision stages and the possible response paths. The decision tree provides good results for classification tasks or regression analyses. With the help of the tree structure, an attempt is made not only to visualize the various decision levels but also to put them in a certain order. For individual data points, predictions can be made, for example, a classification by arriving at the target value along with the observations in the branches. The decision trees are used for classifications or regressions depending on the target variable.

The models can have many hyperparameters and finding the best combination of the parameter using grid search methods. Grid search is a technique for tuning hyperparameter that may facilitate build a model and evaluate a model for every combination of algorithms parameters per grid. We might use 10 fold cross-validation to search the best value for that tuning hyperparameter. These values are called hyperparameters. To get the simplest set of hyperparameters we will use the Grid Search method.

OpenSea is putting in place a new system to spot NFT fakes and verify accounts, in an effort to cut down on the industry's growing fraud problem. In a couple of blog posts, the NFT marketplace detailed what changes users can expect, including opening up verification to more users, automated and human-assisted removal of so-called "copymints" or fake copies of authentic NFTs and changes to how collection badges -- which identify NFT collections with high sales volume or interest -- are doled out on the marketplace. First off, OpenSea will use a two-part system to detect fakes that combine both image recognition tech and human reviewers. The company says its new system will continuously scan all NFT collections (including newly minted assets) to spot any potential fakes. "Our new copymint prevention system leverages computer-vision tech to scan all NFTs on OpenSea (including new mints). The system then matches these scans against a set of authentic collections, starting with some of the most copy-minted collections -- we'll look for flips, rotations & other permutations," wrote OpenSea's Anne Fauvre-Willis in the post.

A trial in which trainee teachers who were being taught to identify pupils with potential learning difficulties had their work'marked' by artificial intelligence has found the approach significantly improved their reasoning. It suggests that artificial intelligence (AI) could enhance teachers' "diagnostic reasoning": the ability to collect and assess evidence about a pupil, and draw appropriate conclusions so they can be given tailored support. During the trial, trainees were asked to assess six fictionalised "simulated" pupils with potential learning difficulties. They were given examples of their schoolwork, as well as other information such as behaviour records and transcriptions of conversations with parents. They then had to decide whether or not each pupil had learning difficulties such as dyslexia or Attention Deficit Hyperactivity Disorder (ADHD), and explain their reasoning.

So, if Machine Learning is the way AI is powered to meet only the last point of acquiring knowledge and storing it for use later, then will this not be "incomplete intelligence"? At the risk of sounding like a non-conformist, Pearl argues that Artificial Intelligence is handicapped by an incomplete understanding of what intelligence really is. AI applications, as of today, can solve problems that are predictive and diagnostic in nature, without attempting to find the cause of the problem. Never denying the transformative and disruptive, complex, and non-trivial power of AI, Pearl has shared his genuine critique on the achievements of Machine Learning and Deep Learning given the relentless focus on correlation leading to pattern matching, finding anomalies, and often culminating in the function of "curve"-fitting. The significance of the "ladder of causation" i.e., progressing from association to intervention and concluding with counter factuality has been the contribution of immense consequence from Pearl. Pearl has been one of the driving forces who expects that the correlation-based reasoning should not subsume the causal reasoning and the development of causal based algorithmic tools.

So, if Machine Learning is the way AI is powered to meet only the last point of acquiring knowledge and storing it for use later, then will this not be "incomplete intelligence"? At the risk of sounding like a non-conformist, Pearl argues that Artificial Intelligence is handicapped by an incomplete understanding of what intelligence really is. AI applications, as of today, can solve problems that are predictive and diagnostic in nature, without attempting to find the cause of the problem. Never denying the transformative and disruptive, complex, and non-trivial power of AI, Pearl has shared his genuine critique on the achievements of Machine Learning and Deep Learning given the relentless focus on correlation leading to pattern matching, finding anomalies, and often culminating in the function of "curve"-fitting. The significance of the "ladder of causation" i.e., progressing from association to intervention and concluding with counter factuality has been the contribution of immense consequence from Pearl.

WARNING--Graphic footage: Fox News correspondent Bryan Llenas has the latest on the investigation from Brooklyn, New York, on'Special Report.' New York City may be rolling out new technology and periodic bag checks to prevent future terrorist attacks, according to the mayor. New York City Mayor Eric Adams spoke with MSNBC's "Morning Joe" on Wednesday about the previous day's terror attack on the city's subway system. The mayor touched on the possibility of new technology on public transportation to prevent similar acts in the future. "With the gun detection devices – oftentimes when people hear of'metal detectors,' they immediately think of the airport model," Adams said.