Classification

What are Decision Trees?

Decision Trees are a type of supervised machine learning model used for both classification and regression problems. They work by splitting the dataset into smaller and smaller groups based on feature values. At each “node,” the algorithm picks the feature and threshold that best separates the data. T

he process continues until the tree reaches a stopping point, like a maximum depth, a minimum number of samples per leaf, or when the data is fully “pure”. The final nodes – leaves, represent the prediction. These are great because you can literally follow the path from root to leaf to see why a certain prediction was made.

Project 2 Dataset

For this project, I’ll be using the Cards Image Dataset Classification from Kaggle. The goal is to predict which card type is shown in each image, making the target variable the “Card Label,” such as the Ace of Spades. I’m also hoping I can also classify the combination of suit and rank. The input features come from the image data itself, either directly from the raw pixels or from features derived through image processing techniques that capture patterns like shapes, colors, and symbols.

Project 2: Predicted findings

From this project, I could learn which types of playing cards are easiest or hardest for a model to classify and identify which features (such as suit symbols, shapes, or text) contribute most to correct predictions. The results could highlight weaknesses in the dataset, like if certain suits or ranks are underrepresented, or if poor lighting and image quality cause higher misclassification rates. On the positive side, a well-performing model could support useful applications like automated card recognition for games or accessibility tools for visually impaired players. On the downside, there are risks such as cheating in competitive card games.