Decision Tree Models

Data Science Interview Deep Dive Question: Q. Can decision trees implement both supervised and unsupervised learning? If so, please explain how they work under the hood and give some example use cases of both. My Answer: Yes, decision trees can be implemented for both supervised and unsupervised learning, each serving different purposes and using different techniques. 1. Supervised Learning with Decision Trees In supervised learning, decision trees use a labeled dataset, where each instance has an input vector and an associated label (target output). The goal is to construct a model that can predict the label of new instances based on the patterns learned from the training data. Decision trees for supervised learning can be used for both classification (categorical output) and regression (continuous output) tasks. - How it Works: -- Node Splitting: The decision tree algorithm starts at the root node and splits the data on the feature that results in the most significant improvement in homogeneity or purity of the target variable. This is often measured using metrics like Gini impurity, entropy in classification tasks, and variance reduction in regression. -- Tree Growth: This process of selecting the best feature and splitting the node is repeated recursively for each child node. The recursion continues until a stopping criterion is met, which could be a maximum depth of the tree, a minimum number of samples in a node, or no further improvement is possible. -- Pruning: Sometimes the fully grown tree is pruned back to avoid overfitting. This involves removing sections of the tree that provide little power in predicting the target variable. -- Examples: --- Classification: Predicting whether an email is spam or not. --- Regression: Predicting house prices based on features like location, size, etc. 2. Unsupervised Learning with Decision Trees Unsupervised decision trees, sometimes referred to as decision trees for clustering, don’t require labeled data. Instead, they organize the data into different groups based on their intrinsic similarities and differences. - How it Works: -- Similarity Metrics: The tree is built by choosing splits that maximize the similarity within each child node according to some feature-based metric. The metric and splitting criteria differ from supervised learning as there’s no target variable to guide the splits. -- Hierarchical Clustering: The process results in a hierarchical clustering of the data points, where each node represents a cluster, and the branches represent the pathway to reaching that cluster. -- Termination: The process can be terminated based on a predetermined number of clusters or some other criteria similar to stopping rules in the supervised setup. Example: -- Clustering Customers: Organizing customers into distinct groups based on purchasing behavior, demographics, etc., without prior knowledge of the group labels.

For a long time, I thought of Decision Trees as the simple model you get through quickly so you can move on to Random Forests or XGBoost, something fundamental but not something worth spending too much time on once you know the basics. I knew how to talk about entropy and Gini impurity, I knew trees split on thresholds, and I knew they overfit if you let them grow too deep, so I assumed I understood them well enough. Writing this article forced me to confront how shallow that understanding actually was. As I worked through how a single tree grows, how each split is chosen, and how easily the structure changes with small shifts in data, I started to see why trees are so unstable on their own. Parameters like max depth and min samples leaf stopped feeling like knobs you tune by habit and started feeling like safeguards against very specific failure modes. That shift changed how I think about ensembles. Random Forests finally clicked as a direct response to tree instability, not just an accuracy boost, and XGBoost made more sense once I saw how boosting relies on shallow, imperfect trees that are carefully regularized rather than powerful ones that memorize the data. Understanding the limits of one tree made the design choices behind these models feel obvious instead of magical. This post came out of that realization. It is about Decision Trees as they actually behave on real data, why they overfit, how pruning and evaluation matter, and why getting this model right makes everything built on top of it easier to reason about. If you have ever dismissed Decision Trees as too simple, this might change how you see Random Forests and XGBoost as well. https://lnkd.in/e7BD52fy (Image Source: https://lnkd.in/e8Nb8RNm)

Interesting paper from Yiğit Ihlamur's team improving on Decision trees with hierarchical clustering and using LLMs for explainability. Decision trees have long been valued for their simplicity and interpretability. But when data contains diverse subgroups—like patients with different risk factors or founders with varied backgrounds—a single tree struggles. Traditional trees apply uniform rules to everyone, often missing critical patterns. Meanwhile, ensemble methods (e.g., Random Forests) improve accuracy but sacrifice explainability by combining hundreds of opaque trees. GPT-HTree addresses these limitations through a hybrid approach. Here’s how it works: - Hierarchical Clustering First: Instead of forcing one tree to handle all data, GPT-HTree begins by grouping similar data points into clusters. For example, in a dataset of startup founders, clustering might separate serial entrepreneurs from first-time founders. This step ensures the model recognizes that different rules may govern each subgroup. - Local Decision Trees: A separate decision tree is trained for each cluster. Because clusters are homogeneous, these trees can be simpler and more precise. Imagine one tree for serial entrepreneurs (where prior exits matter) and another for technical founders (where expertise in a niche domain is key). Each tree adapts to its subgroup, avoiding the noise of irrelevant features. - LLM-Driven Explanations: Finally, an LLM like GPT-4 translates each cluster’s statistical patterns into plain-language descriptions. For instance, a cluster might be summarized as “founders with prior acquisitions and industry tenure.” This bridges the gap between data splits and human insight, making the model’s logic accessible to non-technical stakeholders. Contrasting with Traditional Methods - Single Trees: Limited by rigid, global rules. A deep tree might capture heterogeneity, but at the cost of interpretability. - Ensembles (Random Forests, Gradient Boosting): Improve accuracy but obscure reasoning. They treat the dataset as monolithic, blending signals across subgroups. - GPT-HTree: Balances specialization and clarity. By isolating subgroups, it captures nuanced patterns while keeping trees simple. The LLM adds a layer of narrative, explaining why a cluster behaves a certain way. Why This Matters Interpretability isn’t just about trust; it’s about actionable insight. GPT-HTree doesn’t just predict outcomes—it reveals the underlying structure of the data. For domains like healthcare, finance, or venture capital, where decisions impact lives and livelihoods, this transparency is invaluable. The framework also highlights a broader trend: combining classical techniques (clustering, decision trees) with modern tools (LLMs) to solve problems that pure supervised or unsupervised methods cannot. Paper link in comments Image: Results on what personas succeed in building venture scale companies with their success rate.

*** Decision Tree Learning: Explained and Its History *** ~ Decision tree learning is a supervised learning approach in statistics and data mining. This formalism uses a classification or regression decision tree as a predictive model to conclude a set of observations. ~ Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels, and branches represent conjunctions of features that lead to those class labels. ~ Decision trees where the target variable can take continuous values are called regression trees. More generally, the concept of regression trees can be extended to categorical sequences. ~ Decision trees are among the most popular algorithms, given their intelligence and simplicity. ~ A decision tree can visually and explicitly represent decisions and decision-making in decision analysis. See the post image. ~ A decision tree is a simple representation for classifying examples. For this post, assume that all input features have finite discrete domains and a single target feature called the "classification." Each element of the domain of the classification is called a class. ~ A decision or classification tree is a tree in which each internal (non-leaf) node is labeled with an input feature. The arcs coming from a node labeled with an input feature are labeled with each of the possible values of the target feature, or the arc leads to a subordinate decision node on a different input feature. ~ Each tree leaf is labeled with a class or probability distribution over the classes, signifying that the tree has classified the data set into either a specific class or a particular probability distribution. ~ A tree is built by splitting the source set, constituting the tree's root node, into subsets—which constitute the successor children. The splitting is based on a set of splitting rules based on classification features. This process is repeated on each derived subset in a recursive manner called recursive partitioning. The recursion is completed when the subset at a node has all the same values of the target variable or when splitting no longer adds value to the predictions. ~ In data mining, decision trees can also be described as the combination of mathematical and computational techniques to aid the description, categorization, and generalization of a given data set. ~ There are two types of decision trees: * Classification tree analysis is when the predicted outcome is the class (discrete) to which the data belongs. * Regression tree analysis is when the predicted outcome can be considered an actual number (e.g., the price of a house or a patient's length of stay in a hospital). ~ The classification and regression tree (CART) analysis is an umbrella term for either of the above procedures, first introduced by Breiman et al. in 1984. --- B. Noted

Decision trees are a fundamental tool for every Data Scientist. But for 3 years, I was hesitant to use them. In 3 minutes, I'll share what they are (and how they became a key part of a $15,000,000 lead scoring model). Let's go: 1. Decision Tree: A decision tree is a graphical representation used for decision-making and data analysis. It resembles a tree structure and is commonly used in machine learning, specifically in classification and regression tasks. 2. Structure: A decision tree consists of nodes and branches. The top node is known as the root node, and it represents the entire dataset. Decision Nodes: These are where the splits happen, based on a certain condition or attribute. Leaf/Terminal Nodes: These nodes represent the outcome of the decision process. 3. Splitting Criteria: This is the method of choosing the attribute for splitting the data at each node. Common criteria include Gini impurity, Entropy (information gain), and variance reduction. 4. Why I was hesitant to use them: I stuck to linear models for the longest time. I understood Linear Models, and I didn't trust Tree-Based models. I knew they were prone to overfitting which had bitten me in the past. And I also knew they couldn't predict over the max/min of the data so extrapolation was a weakness. So why did I start? 5. The Random Forest (Decision Trees on Steroids 💪 ): In 2016, I'd been monitoring several DS competitions on a website called Kaggle (it was new to me back then). And I stumbled upon a challenge where Random Forests were being used. Come to find out, a Random Forest was an ensemble of decision trees that boasted much higher stability and better performance. So I gave it a whirl. 6. Improving my $15,000,000 Lead Scoring Model: Up until now I'd made my company a ton of money, scaling from $3M to $15M in 2 short years. All with a simple Logistic Regression Model! But what made it even better was using Random Forest, which was all based upon Decision Trees. ✅ Performance increased! ❌ But explainability suffered. 7. Explainability (Random Forest Black Box): A problem surfaced, Random Forest was a Black Box. The solution: Use Decision Trees to help simplify my Random Forest so I could trust the model and explain it to management. Combining Decision Trees and Random Forest created a win-win that further improved my $15,000,000 Lead Scoring model. === Want help improving your data science skills? 👉 Free 10 Skills Webinar: I put together a free on-demand workshop that covers the 10 skills that helped me make the transition to Data Scientist: https://lnkd.in/gbEBVf5f 👉 ChatGPT for 10X Faster DS Projects: I have a live workshop where I'll share how to use ChatGPT for Data Science (so you can complete projects 10X faster): https://lnkd.in/gCvh6UAy If you like this post, please reshare ♻️ it so others can get value.