The Power of Decision Trees

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A Decision Tree is one of the most used algorithms in tabular data use cases 🤖 Whether it's finance or healthcare it's still super useful due to its explainability📝 Here's everything you should know about DTs...

What is a decision tree? 🌲 A decision tree is a popular supervised machine learning algorithm used for both classification and regression tasks.

It is a flowchart-like model where each internal node represents a test on a particular feature, each branch represents the outcome of the test, and each leaf node represents a class label or a numerical value, depending on the type of problem (classification or regression).

The decision tree algorithm works by recursively partitioning the data into subsets based on the values of different input features.

At each internal node, the algorithm selects the feature that best separates the data into its classes or reduces the variance in the target variable (for regression). This process is known as the "splitting" process.

Workings of Decision Tree🌳 DTs work by splitting each feature into a random row creating subsets of datasets. At each split, DT calculates an error metric to measure how well the split is and decides further split until the stopping criteria is archived.

Calculation of the Gini index (|impurity criteria) 🤖 It quantifies the probability of misclassifying a randomly chosen element if it were randomly classified according to the class distribution in a particular node.

A lower Gini index indicates a more pure node with predominantly one class, while a higher index implies higher impurity and a mix of different classes in the node.

Homogeneity in Decision Trees 📊 A node is considered homogeneous if all the data samples it contains belong to the same class (in a classification problem) or have the same value (in a regression problem).

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