In today’s session, we discussed the decision tree algorithm, a form of supervised learning that can be applied to both classification and regression tasks. This algorithm constructs a tree-like model of decisions, where:
– Internal nodes represent the features of the dataset.
– Branches represent decision rules.
– Leaf nodes represent the outcome or decision made after computing all features.
A decision tree is built by splitting the source set into subsets based on an attribute value test. 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 the same value of the target variable, or when splitting no longer adds value to the predictions.
This algorithm is particularly powerful because:
– It includes automatic feature selection.
– It doesn’t require much data pre-processing.
– It is easy to interpret and understand.
Decision trees also form the building blocks of Random Forests, which are an ensemble of decision trees trained on various sub-samples of the dataset. This makes Random Forest one of the most robust machine learning algorithms available, capable of performing both classification and regression tasks with high accuracy.
During the training of a decision tree, it looks for the feature that best separates the data into classes. This is done using measures like Gini impurity or entropy, which provide a way to quantify the best split.
The specifics of how we will apply the decision tree algorithm to our dataset will be detailed in future updates.