Greetings! In today's lesson, we dive even deeper into the intriguing realm of machine learning. Our prime focus is explaining and applying hyperparameter tuning to decision trees. Decision trees are a critical class of machine learning algorithms frequently used for classification and regression tasks. To enhance the performance of our decision tree classifier, we'll leverage the power of Scikit-learn's GridSearchCV tool. It aids us in fine-tuning the hyperparameters, contributing significantly to model optimization.
Starting with the basics, Decision Trees are part of Supervised Machine Learning algorithms predominantly used for classification and regression tasks. As their name suggests, these algorithms construct a tree-like model of decisions. These decisions are based on particular conditions derived from the input features, leading to a final prediction about the target variable.
An important aspect of Decision Trees is their interpretability. They are not simply a "black box" model - you can visualize the decisions being made, which is incredibly helpful for understanding why the model makes the predictions it does.
Consider a Decision Tree model as a flowchart for making a decision. For example, if you want to predict whether you would like a particular type of movie, the decision tree could use features such as the film's genre, the director, how much you like the lead actor/actress, and so on, ultimately leading to a decision: to watch or not to watch.
Like most machine learning algorithms, Decision Trees employ hyperparameters that you can tweak to enhance the model's performance. These hyperparameters accurately control two primary factors - how the nodes in the tree split and when the tree growth ceases. Here, we will concentrate on two main hyperparameters in Decision Trees:
max_depth: It specifies the deepest level of the Decision Tree. Deeper trees mean more splits, thereby capturing more information about the data. This increases the complexity of the model but is more prone to overfitting, i.e., performing well on the training data but poorly on unseen data.
