**calculate_entropy(labels) -> float:

• Purpose: Calculate the entropy of a list of classification labels.
  • Entropy measures the randomness in the set, which is crucial for deciding on splits in a decision tree.
• **Parameters: labels
  • list of classification labels.
• **Process:
  • Counter to count occurrences of class labels, calculates their probabilities, and computes entropy according to
  • the formula (Entropy = -\sum (p \times \log_2(p))).
• **Returns: The calculated entropy as a float.

**Conditional Statement involving low_salary_count + high_salary_count:

• used intended to calculate the total entropy for salary data (either "High" or "Low" salaries) by using the calculate_entropy function.

**divide_entropy(subsets) -> float:

• Purpose: Calculate the weighted average of entropy for a given division of data subsets. - its useful in the decision of tree algorithms to measure the effectiveness of a split.

• Process: - the entropy for each subset by weighing the subset's proportion to the total number of instances.

• Returns: The weighted average entropy.

**DecisionTreeNode Class:

• Each node can either be a decision node - feature_index and a feature_value
  • or a leaf node- a predicted class label for that branch.

divide_dataset Function:

• Splits the dataset and corresponding labels based on a specified feature index and its value.
• function is essential for recursively dividing the dataset as the tree grows, ensuring each child node gets the correct subset of data.

build_tree Function: - checks for base cases- the dataset is empty or all instances belong to the same class). - It then finds the best feature to split on by calculating the information gain for all features. - Next, it creates a new node based on the best feature and recursively builds the tree for each subset of the dataset resulting from splitting on the best feature.

Error Handling and Validation - Checking if data and labels are lists, and if their lengths match. - Ensuring feature index is within the bounds of the dataset. - Handling cases where there is an empty dataset or labels/features are missing.